How NOT to be an intelligent automation failure

How NOT to be an intelligent automation failure

According to a recent survey by IDC, of the organizations already using artificial intelligence solutions, only 25% have developed an enterprise-wide AI strategy. What’s holding them back? Well, according to that same survey, most failed AI projects occur due to lack of skills, staff and resources, as well as unrealistic expectations. So, how can you avoid and/or overcome these common roadblocks with your own intelligent automation implementation? The following five tips should help set you up for success.

Start with the right partner/product.

First and foremost, you want to make sure the technology vendor you choose to work with is reputable and offers a robust, agile and scalable product. The right intelligent automation platform can easily make up for three of the main causes of implementation failure: lack of skills, resources and staff. But remember – not all products or vendors are created equal. Look for a partner that has a proven track record with other satisfied clients and offers plenty of support after the sale, not just before it.

Assemble the right team.

If enterprise-wide proliferation of intelligent automation is your goal, then handing it off as a part-time project to team members who are already juggling a full workload won’t help you achieve it. Being successful with AI requires an all-hands-on-deck approach, and for that, a dedicated team is necessary. If you can’t afford to hire experts? No problem. Train and reskill your existing staff. Just don’t make it an afterthought, or the results will suffer.

Align your goals (and be realistic).

As mentioned, one of the main reasons an intelligent automation initiatives fails is unrealistic expectations. If you’re heading into the project thinking you’re going to automate everything right out of the gate, there’s a massively good chance you’re going to end up disappointed. Instead, start slow and work your way up. Identify and automate the areas of your business that will produce the quickest and most impactful ROI, and that directly support the company’s overarching goals and objectives.

Take time to understand AI decisions.

When adopting artificial intelligence, it’s crucial that you are able to understand the ‘why’ behind each decision made by AI. Taking a lackadaisical approach simply won’t cut it. For instance, if your AI is automatically approving some applications but declining others, you need to be able to confidently point to the factors that went into those decisions. This is important because while AI has come a long way, it’s not 100% infallible. Being able to quickly and effectively address and correct mistakes is key.

Keep it real.

Let’s face it. Change can be a scary thing. And perhaps nothing is more fear-inducing to a human employee than the idea that intelligent automation is going to render them irrelevant. You can’t expect to successfully achieve organization-wide AI if you don’t have the support and cooperation of everyone – especially those who will be directly impacted. People will inevitably have questions. Be open, honest and transparent. Communicate clearly and consistently how the impending changes will not threaten but instead improve their lives. Change doesn’t have to be a roadblock, as long as you manage it.

Without question, intelligent automation, powered by AI and machine learning, is poised to become a complete game-changer. The challenge lies in an organization’s ability to effectively implement it. By following the five tips listed above, you can avoid the common pitfalls plaguing so many others and position your company a step ahead of the game.

The best time to start is now! Download your free 30-day trial of Ayehu NG and put the power of intelligent automation to work for you!

Three Steps To Prepare The Enterprise For The Digital Workforce In 2020

Article originally published in Forbes Technology Council.

There’s no longer any uncertainty or ambiguity. Automation absolutely, positively will impact the way every one of us works. The degree to which that impact occurs will vary, but make no mistake: Humans in every industry and position, from warehouse workers to C-suite executives, will someday soon be working alongside digital workers (a.k.a. virtual agents).

Just what will this future digital enterprise look like? The answer to that lies in how organizations implement artificial intelligence.

The ‘How’ Vs. The ‘What’

For many workers, the way that automation and artificial intelligence technologies are adopted will be more effective than the technology itself. The same goes for organizations as a whole. To succeed in the digital age, business leaders must begin to shift their viewpoint from opportunistic to a more systematic approach. In years past, automating on an ad hoc basis was sufficient. Over time, however, that strategy led to silos that were not adequately governed, nor were they scalable.

The future of automation in tomorrow’s workplace must be rigorous and robust, policy- and data-driven, and, above all, enterprise-centric. In other words, it’s not so much about the “what” as it is about the “how.” This will be the main differentiator between organizations that succeed in achieving digital transformation and those that fall irreparably behind.

Three Steps To Success With Intelligent Automation

1. Build. New technologies like artificial intelligence and machine learning will inevitably affect some workers in adverse ways. This has always been the case, as people continue to be displaced from one economic sector to another. In fact, according to one estimation by McKinsey, up to 30% of the global workforce (and between 400 million and 800 million workers) could be displaced by automation by the year 2030.

But while some jobs will ultimately be eliminated, the current and ongoing technological innovation we are experiencing will simultaneously create new opportunities.

Perhaps it is more than fitting that a fictional Borg from the futuristic Star Trek series uttered the infamous words, “Resistance is futile.” Like it or not, AI and automation technologies are already having an impact on the workplace, and they’re not going away any time soon.

The future of work will ultimately belong to those individuals who are willing to embrace and leverage artificial intelligence to their advantage. This may come in the form of self-automation — that is, the foresight and desire to automate portions of one’s own job in the interest of productivity and efficiency. Organizational leaders can and should meet them in the middle by seeking out key employees who show promise, optimism and a willingness to adapt and reskill, if necessary.

Investing in human capital with the ultimate goal of developing an automation center of excellence will create a compromise between top-down mandated automation and bottom-up, enthusiastic support and participation. This is the ideal scenario and one that will drive ongoing innovation and success. Some key roles to focus on for the future include:

  • Automation architects.
  • Automation engineers.
  • Site reliability and DevOps engineers (SRE).
  • API product managers.
  • Data scientists.

2. Standardize. With the right people and teams in place, the next step toward leveraging intelligent automation for digital transformation should involve the standardization of processes and the creation of best practices.

To start, the focus should be on delivering continuous value rather than aiming for one major change. This is achieved via strategic increments.

Centralized governance will then help to ensure ongoing compliance and support future growth and expansion.

3. Invest. Many will find it surprising that technology is actually the final piece in the automation puzzle. This is due in large part to the old-school, opportunistic way of thinking. The new recommended approach is one that involves a strategic cultural change and focuses on people and processes first, and then tools and technology.

Once these first two factors have been determined, the search for the right automation platform can begin. Ideally, the criteria should include no-code or low-code solutions that are both robust and agile. This will enable the eventual proliferation of automation across the entire enterprise while also supporting the future growth and changing needs of the business and/or industry.

Closing Thoughts

What will the workplace of tomorrow look like? For human workers, it will be markedly different and require new skills and greater adaptability. For the enterprise, it will be a composite of real and artificial intelligence — humans and machines — working together toward a common goal of innovation and success.

Dare to take risks despite your fear. Organizations and their employees who approach these challenges with eagerness and optimism, a willingness to adapt and evolve, and the ability to strike the ideal balance between humans and machines will ultimately be the ones who rise to the top.

Building Intelligent Automation Into Your Organizational Culture

In order for intelligent automation to be truly successful and produce sustainable results, it can’t be a one-off project that is exclusive to the IT department. It has to be woven into the very culture of the organization and fully embraced across the entire company. But changing corporate culture is much easier said than done. How can you incorporate automation so that it becomes an integral part of the everyday work environment? Here are a few suggestions to get you started.

Get buy-in from top leadership. Cultural changes typically start at the top and trickle downward, so make sure that everyone in a leadership role within your organization understands the benefits of intelligent automation and why it’s so critical that it become a part of the underlying atmosphere of the company as a whole. Once they’re on board, it’s time to start leading by example.

Sell the benefits. If you want your company culture to embrace automation, you have to make everyone in every position aware of how it will benefit them directly. In other words, show what’s in it for them. Otherwise, you will lack the support needed to make the final shift. Remember, intelligent automation isn’t just about those running your help desk. Things like self-service automation also provide enhanced flexibility, autonomy and empowerment to the end-user. Get the message out.

Identify and address obstacles. Change management is challenging, especially when it involves the evolution of an entire corporate culture, but it’s not impossible. You just have to understand what’s standing in the way so you can overcome those obstacles. For instance, if your employees are scared that intelligent automation will make them obsolete, they will resist. You have to address and quell that fear head on by showing them the opportunities it will bring for new roles, such as Automation Engineer, and the ability to do more with less.

Incentivize and reward. Culture change happens much more smoothly and effectively when it’s not shoved down the throats of your employees. Instead of simply telling them and expecting them to adapt, make them a part of your company’s evolution. Not only will this help them better understand the reasons behind the change, but the buy-in will create a much stronger foundation for the shift across the board.

Keep it fluid. The beauty of intelligent automation, and technology as a whole, is that it’s constantly changing and improving. A corporate culture is much the same in that it should be something that can be molded and enhanced as needed. Keep an open mind and make modifications where necessary. As long as you’ve got a solid foundation to work with, the only direction you can go is up.

Have you been successful in weaving intelligent automation into your organizational culture? Please share your insight, advice and tips in the comment section below. And don’t forget to launch your free product demo of our Next Generation Automation & Orchestration platform, powered by AI. It’s something you must experience for yourself!

Is AI Killing Jobs….or Creating Them?

Ask a roomful of people how they feel about artificial intelligence in the context of jobs and you’ll undoubtedly received a mixed bag of responses. Some will undoubtedly express their concerns that AI is poised to destroy employment as we know it today. Others will take a more optimistic approach, viewing AI as a tool to help us work more efficiency and accomplish things we couldn’t do with human workers alone. Even the various analysts and economists range widely in their predictions of what role AI will ultimately have in the future of work.

The truth, as is quite often the case, lies somewhere in the middle. There’s no question that intelligent automation will eliminate some jobs, impacting just about every industry and sector across the board. At the same time, however, AI will create new jobs, both in categories we’re familiar with as well as many more that have yet to be developed.

AI = Job Killer?

Automation is nothing new. It’s a technology that’s been embraced and lauded by organizations for decades upon decades. The key differentiator here is the introduction of the word “intelligent.” It’s the cognitive abilities afforded by AI and machine learning that will ultimately enable businesses to optimize their use of human labor. And that’s where the shakeup will inevitably occur.

In fact, a shift has already begun – particularly in areas where the work is highly repetitive, regulatory intensive and prone to error. Why pay humans to perform work that could easily be carried out by an intelligent bot – especially when that bot is capable of understanding the meaning and context of the information at hand?

So, does this mean people are being eliminated from the workplace entirely? Not so fast. In fact, there are plenty of categories of employment that will remain relatively untouched by AI. For instance, many professional services categories will remain intact as they still require a level of responsiveness that cannot yet be replicated by artificial intelligence.

AI = Job Creator.

Studies indicate a surprisingly positive view of AI’s role and ultimate impact on tomorrow’s workplace. In fact, a recent survey revealed that 75% of U.S. workers do not view their jobs as being at risk of elimination – at least not within the next decade.

Additionally, the vast majority (87%) of workers say they wish their employers would automate more tasks and processes. Why? Because they recognize the promise that AI brings in terms of improved efficiency and increased productivity. In other words, most people understand that AI will make their work lives better, not worse.

But, what about those repetitive, error-prone tasks that are already being shifted from human to machine? Won’t those workers end up in the unemployment line? Not if they are willing to change gears. In fact, the rapid adoption of AI and intelligent automation is already creating exciting new roles and opportunities.

Part of the challenge here is that it’s difficult to fathom jobs and employment categories that may not yet exist. Thinking back 20 years, the concept of a role such as social media manager was completely foreign, yet today it’s something most organizations have. Likewise, looking forward 20 years into the future, there will undoubtedly be whole new sectors of the economy that do not exist today. And along with those emerging opportunities, human workers will also need to adapt and evolve.

Without question, the world is experiencing a revolutionary shift, thanks in large part to artificial intelligence technology. Thankfully, the magnitude of the impact that shift will have on the future of work is still largely within our control. Those who are at the greatest risk of redundancy can provide themselves with a safety net by proactively reskilling and reinventing themselves.

Not sure where to begin? Our Automation Academy is a great place to start. Enroll today!

4 Steps for Selling Intelligent Process Automation to the Masses

Seasoned leaders know that, when beginning any significant project, there are two different paths that can be taken. Path number one offers the shortest route from point A to point B. This is to simply force-feed the project to everyone, essential saying, “We’re doing this and that’s that.” The second path, on the other, may be a little less direct and take a lot longer. On this journey, time is taken to explain the strategy and the reasoning behind it. In other words, saying, “Here’s what we’re doing and why.”

Which of these paths do you think will yield better results? If you chose the second path, chances are you’ve experienced both options before and know firsthand that getting people onboard with change is almost always the wiser choice. Few industries are as familiar with the correlation between major change and feelings of fear, skepticism, resistance and other challenges as leaders in the IT realm. Implementing intelligent process automation is no different.

What’s the best way to overcome the many uncertainties and misconceptions that could delay or derail your automation project? Let’s take a look.

Appeal to their self-interest.

Most people won’t get fully behind a project unless and until they know how it will directly impact their lives – essentially, they want to know what’s in it for them. Self-preservation is human nature. Appealing to this natural instinct can help make your argument much more persuasive. Instead of simply announcing that you will be introducing intelligent process automation into the mix, show them how that change will benefit them.

Will automating a particular workflow finally put an end to those middle-of-the-night phone calls? Will it allow some employees to eliminate low-skill, manual tasks from their workload, freeing them up to focus on more strategic and meaningful work? Will learning how to work alongside artificial intelligence enable ambitious team members to develop new marketable skills that they can use to further their career?

Figure out what’s in it for each of the individuals or teams you are presenting to, as well as how intelligent automation will ultimate benefit customers and the company as a whole, and then communicate that to the masses. Paint a picture of what’s currently causing issues and then demonstrate how automation can help. By the way – the same concept applies when making the case outside of the IT department, to non-technical stakeholders, for example. Just go easy on the jargon.

Connect your proposal to specific business goals.

A big part of making a strong and support-worthy case for anything, really, involves getting people to understand that you’re not simply chasing trends. In other words, you’re not just automating for the sake of automating. If people sense that’s the case, they’re going to lose confidence and likely provide even greater resistance – especially those who are directly impacted, such as the IT team.

The case for intelligent process automation must be driven by a specific business demand, whether it’s reducing expenses, improving service levels, gaining competitive advantage, etc. Unless it’s a core competency of the organization, no automation endeavor should be a means unto itself.

If you want people to back your plan, you need to align it with specific business goals and then clearly and accurately convey those connections. Lay out these goals and explain, step-by-step, how automation will help the company achieve those goals.

Break down your plan into manageable milestones.

One major reason why many automation projects fail is because they are simply overwhelming undertakings. Even if your goal is to automate everything (or close enough), attempting to do so in one fell swoop is simply not realistic nor is it a sustainable strategy.

You’ll make a much stronger, longer-lasting argument when you develop a plan that breaks down your project into smaller, more manageable increments. This also allows for more flexibility to be able to adapt and iterate as needed along the way. At Ayehu, we almost always recommend starting with tasks and workflows that offer the quickest and most measurable wins. This will enable you to continuously prove value and gain ongoing support as you begin to proliferate automation further throughout the organization.

Identify smaller areas where automation will have the biggest immediate effect and then work your way outward from there. Remember, as they say, the proof is ultimately in the pudding. Once you’ve got those smaller wins under your belt, you’ll be in a much better position to sell the big-picture benefits as well.

Sing your own praises.

Well, not necessarily your praises, but those of your automation project. If you’ve followed the steps above, you should begin to generate ROI relatively quickly. It’s in your best interest to promote those positive results early and often. There is no case more convincing than one that features real-world, definitive and measurable results.

This step is especially important for instances where skepticism still abounds. People can resist change and choose to doubt anticipated benefits of intelligence process automation all they want, but this becomes markedly more difficult when they can see and experience those benefits firsthand.

Not only will continuously promoting positive results quiet the critics, but it will also lay the groundwork for automating even more tasks and workflows in the future, which will ultimately lead to becoming a self-driving organization.

Get started on your journey to successful adoption of intelligent process automation today by downloading your free 30-day trial of Ayehu NG.

Episode #33: How To Upscale Automation, And Leave Your Competition Behind – transformAI’s Lee Coulter (Part II)

January 15, 2020    Episodes

Episode #33:  How To Upscale Automation, And Leave Your Competition Behind (Part II)

In today’s episode of Ayehu’s podcast we interview Lee Coulter – CEO of transformAI & Chair of the IEEE Working Group on Standards in Intelligent Process Automation

Automation in the enterprise has proven efficient, cost effective, & mature enough that early adopters have begun scaling up their deployments to increase returns & amplify their competitive advantage.  The long-term repercussions of this will likely widen the gap between market leaders & laggards.  In the short term, this expansion will raise questions about the best way to approach sweeping organizational change management for what is proving to be one of the most profound changes organizations & their personnel will ever go through. 

In Part II of this 2-part episode, Lee Coulter, CEO of transformAI shares more of his insights on upscaling automation, including how to prepare for & mitigate common stall points, the one department whose inclusion in an enterprise automation implementation can be most critical to avoiding its derailment, and his predictions for the biggest disruptions we’ll see from intelligent automation over the next few years. 



Guy Nadivi: Welcome back to part two of our two-part podcast with Lee Coulter, CEO of transformAI and chair of the IEEE Working Group on Standards in Intelligent Process Automation. In this segment, Lee continues sharing his insights with us on workforce re-skilling and up-skilling, the stall points that can be lethal to scaling automation in an enterprise, and his predictions for what disruptions we’ll see from intelligent automation over the next few years.

Lee, I recall from our interview when you first came on the show in 2018 that you’re a big advocate for organizations to include re-skilling and/or up-skilling, in their automation strategy. So how does, or should, workforce re-skilling or up-skilling factor into the game plan for organizations massively scaling up their intelligent automation operation? Lee Coulter: Yeah, so I, you know, I spent five months writing the paper on change management and I do encourage people to go and read it. It’s a bit of a read, I think it’s 10 or 12,000 words, but it really goes into depth on different aspects of change management. And one of the things that I really harp on is the importance of having an overt and explicit conversation with your workforce, about your intentions, about your successes, and about what you’re going to do with the benefits that you achieve with automation. And one of those inevitably, is that you will be looking for different kinds of skills as you take certain repetitive and task-based roles out. You need less people to do those and you need to have a fact-based and visible strategy for addressing the fear, the natural fear, that people have that they will not be qualified for the jobs that remain or the new jobs that are created. And so having proof points, and I get very specific in my recommendations, around in your early implementations that you take two, or three, or four people and you very publicly send them away to school for 12 weeks to get a new certificate. That you put them in intensive up-skilling and job shadowing, but that you have some sort of people that can actually say to the rest of the organization, “Look, my role materially changed in the org. I can look you in the eye and say, the organization took care of me and I’m now doing a job that I’m happier with. It’s more engaging, more exciting, and I feel like I have a better career future.” And you need to have those visible proof points to make an enterprise bet on this. And there are lots of examples of companies that are making enormous bets on automation that have made public statements and huge PR announcements about their investments in up-skilling centers and retraining centers. And while I don’t necessarily think that that’s necessary for everybody, I do think it is essential that you have an explicit conversation and you have proof points that people can look at that say when two thirds of my current role are automated, I can depend on the fact that my friend in another department has gone through the same thing and has a success story to tell on the other side of it.

Guy Nadivi: Speaking of proof points Lee, our audience always enjoys hearing about real world examples. Can you tell us about one or two interesting outcomes transformAI facilitated for organizations by scaling up their intelligent automation operations?

Lee Coulter: Yeah, there’s a lot. Let me pick a couple out. In one of our clients, they made a very public effort at community involvement. And so we do these things called bot-athons, and they have the interesting effect on the organization in that they’re now involving hundreds of people across the organization who get to learn more about what automation is, what it does, what it can do, the kinds of use cases that it can attack. It gives you a stage to have change management conversations, to have up-skilling and re-skilling conversations. And of course it gives you hundreds of use cases to keep the funnel full in your automation program. And whether or not any of those use cases actually end up being viable is less important than the role of raising awareness, creating excitement, and creating a platform to discuss what the automation program is, how it works, where it’s being deployed, and to create this ongoing dialogue about how is the program doing? How are the people fairing in terms of their role in the automation program?, etc. So that would be one that I would offer. Another one would be in where we have a very publicly solved super painful problem. And in this case it was a combination of efficiency and effectiveness. And the effectiveness improvements in a particular process for one of our larger customers resulted in demand coming from the field that the business come out and look at other high friction processes. And this is, that’s a huge success story. If you can find significant use cases that have both a… an efficiency and an effectiveness gain that remove a high friction process from production that your end users out in the business, will feel you’ve now created a platform on which you can talk about rapid scaling outside of your current boundaries.

Guy Nadivi: You were involved in the Shared Services and Outsourcing Network publishing of another Global Intelligent Automation Market report in the second half of last year that focused largely on “stall points”, that is bumps in the road restrictions or other obstacles that stall deployment of automation. Your report identified 13 of these stall points. And I’m curious, Lee, if there are, let’s say 3 that stand out as potentially the biggest stall points you’re likely to encounter when massively scaling up an organization’s intelligent automation operation.

Lee Coulter: You know, it’s interesting Guy that report… And it’s another one I absolutely encourage folks to read. I wrote two reports that year and they’re both aimed at the same topic, which is enterprise scaling and total program yield. And the first one is targeted at those who are just starting out. So kind of how to do it right. The second is targeted at those who are already underway and how to do a diagnostic on your program. And what are the most common divots in the road that you’re going to find in your journey. And I would say that of the 13, at least 11 are inevitable. Some of them, there’s a handful of them that I would call potentially lethal. But they actually have an opportunity to kill your program for at least 18 months. And if a program is stalled for 18 months it really takes a huge effort to get it restarted. So they’re catastrophic. There are these catastrophic stall points where they can come very close to killing the program, where it will require kind of a complete resuscitation. And I’ll give you a couple of examples. One is failing to include audit. So if audit isn’t… internal audit in particular, if they’re not fully aware and inside your governance, and inside your program, and providing advice and guidance. And there’s some sort of a process failure that includes a piece of automation and automation ends up in an audit report that goes to external audit and the audit committee. This I can just virtually guarantee you that if the C-Suite isn’t aware, if the controller isn’t aware, audit committee isn’t aware, the internal auditors aren’t aware, the back draft consequences of that, that’s a catastrophic stall point. So there’s a very specific and delicate way in which you bring that part of the organization into the fold, make them part of governance and actually make them part of the use case back and backlog management. They can in fact, be a huge ally and there’s a whole bunch I can say about how to make that work. That’s an example of a catastrophic stall point. Similarly, there’s the role of IT, very specific here. One of the most consistent findings of stalled and failed programs is it is… and for all of you in IT, this is not necessarily a bad thing, but when IT doesn’t have the right role because this is a business program and not an IT project and it is digital labor, Finance would no more hire IT to perform fixed assets or cash applications in accounting, then they would hire IT to do journal entries. So it’s digital labor. And so this enablement role for IT. When IT smells, bits bytes and when people are accessing their systems that they’re accountable to maintain, costs that they’re accountable to control, et cetera, et cetera. So the role of IT is something that needs to be very deliberately discussed. How you set up your center of excellence, how you scale the program, how you manage credential and security and access, all of these things to ensure the integrity of IT general controls. There are another place for our friendly auditors to come in and also help you. These are stall points that fall into the catastrophic bucket. There are more things like a design authority and others that are very significant, but those are two that I would say are catastrophic if not handled properly.

Guy Nadivi: Lee, we’ve touched upon the fact that in addition to being CEO of transformAI, you’re also the chair of the IEEE Working Group on Standards and Intelligent Process Automation. In May, 2019, IEEE 2755.1-2019, the IEEE Guide for Taxonomy for Intelligent Process Automation, Product Features and Functionality was approved for publication, and this standard defined and classified about 150 features and functions across five core areas of technology capability. First off, congratulations on getting that passed. And second, I can certainly understand the standard’s value proposition if my company is just starting to evaluate automation vendors and needs a tool to help make an apples-to-apples comparison among those vendors. Can you tell us though, how can this standard help me if my organization’s automation initiative is already well underway?

Lee Coulter: So Guy, that was a two year effort. And I think for those who have been involved with it or used it, it’s groundbreaking in that it creates an absolutely objective yardstick, a ruler, measurement approach to understanding what a given product is/isn’t, what it’s capable of or not. And for all of those who are looking at evaluating any sort of product, it’s a turn to page one, read how to use the guide, and then hand it to the people who are responding or that need to prove to you or to… It even provides a part of the standard is why is a given feature or function important? So there’s an educational component in there. So even if you are already underway, it’s a way for you to get into a conversation with your product provider about their technology roadmap and the features and functions. Some of it you may look at and say, “Hmm, well I don’t even know if that feature function is a part of the product that I own currently. Let me have a conversation.” And if it isn’t part of it, then there’s a conversation about what is the technology roadmap to bring that functionality to me, because obviously it was present enough in the population of industry products that it made it into the standard. So it’s a great way to engage and understand what’s available in the market. And to understand where your product provider is in their technology roadmap in bringing you more advanced capabilities. So whether you’re just starting out, or you’ve already gone some distance down the road, it’s a way to have a really, really fact-based conversation with your provider about the functionality that you’re either going to purchase or have purchased.

Guy Nadivi: Okay. That is definitely worthwhile. Now as chair of that IEEE working group, Lee, you’re in a unique position to look over the horizon and see what the future has in store for us. Got any predictions you’d like to make about what are going to be some of the biggest disruptions we’ll see in intelligent automation three, five, or 10 years from now?

Lee Coulter: Well, I can tell you 10 years from now my crystal ball gets really fuzzy. We are in a unique period of multiple exponential technologies converging. And if you look at where each of these technologies are, they’re in their 50th and 60th year of exponential doubling of capability. And so it gets really hard to look out 10 years from now. But if I look out the next two to three, and in fact the paper that I just published, I do offer some insights as we move from task automation to intelligent automation, Crossing the Data Chasm. That’s the title of the paper that was just published a week or two ago. And I offer some thoughts about the changing landscape of intelligent automation. Because as we move from task automation to intelligent automation, all of a sudden we have to engage with unstructured data. Unstructured data comes from interactions with humans, with images, with recordings, with documents. And these really determine or have determined heretofore the boundaries of the size of the use case that can be transformed with intelligent automation. So my immediate predictions are that the toolmakers are increasingly providing either internal capability or access to highly sophisticated external capability that includes things like optical image recognition, natural language processing, sentiment analysis. The ability to rapidly create and deploy data models for predictive task orchestration that dramatically expand the size of the use cases that can be transformed with intelligent automation. So in the next one to two years, what we’re going to see is an increasing focus on common use cases that can be attacked with intelligent automation. And then we’re going to see the productization. So if I look out five years from now, in the same way that Salesforce standardized CRM for the world, we’re going to see a whole host of use case enablers or work transformers built on intelligent automation technologies that will standardize the way we do accounting, the way we do controls and audit, the way we do supply chain. And we’re going to increasingly see these things are entirely digital from end to end where we have an entirely digital supply chain and a fully digital relationship with all of our suppliers. We’re going to see a big focus on taking friction out. And what we’re really going to see is the difference between the leading edge of the early adopters, or the early majority, and what it’s going to cost an organization if you’re a late majority or laggard. Because quite frankly, the competitive advantage that is going to be made apparent by those who are active in intelligent automation now and those that start three years from now, could potentially be lethal from a survivability perspective. So those are a few of my predictions. I guess I’ll offer one more, which is that the prevalence of the Chief Data Officer as a role, as a thing, will grow to be as common as a CFO in the next five years. An organization’s data and knowing how to manage it, and how to evaluate it, and how to value it and to create value from it, is going to become essential for all organizations. So I think that the role of Chief Data Officer, because it’s going to be so essential to mine that data for insights that can change the nature of a business and its operation will be just essential for survival in the future.

Guy Nadivi: That prognostication’s got to be a bit anxiety inducing for those laggards. So finally, Lee, I should ask for the corporate presidents, CEOs or any IT executives listening in, what is the one big, must have piece of advice you’d like them to take away from our discussion today with regards to massively scaling up their intelligent automation operations successfully?

Lee Coulter: Yeah, Guy, I’ll offer your listeners the same… I’m doing a webinar on December 12th. It’s a CPE-eligible course and anybody can register for it. But the big piece of advice that I give as the takeaway at the end of that is the same one I’ll give all of your listeners here. Which is this is not something that can wait. This is industry 4.0. These are the early steps in the digital transformation. These are the steps that will help your organization learn how to do task automation and intelligent automation and move into the world of data and put you on a trajectory. And it’s a business led activity. And IT has a role. And the time is now, this is not hype. It’s real. It’s transformational. And if you don’t have somebody with a title of “something of automation”, if you don’t have a data officer, if you don’t have somebody leading digital transformation, these would all be kind of the alarm bells to get you moving, to get smarter about what this is. It is not the same old, same old. It is not hype. It’s reality. And it’s moving really, really fast. And if you don’t get onboard now, it sounds kind of like doom and gloom, but the average tenure of a company on the S & P or on the Dow Jones, or even living on the NASDAQ is going down every year because of the disruption that is prevalent in all industries. So my final word of advice is if you’re already started, pay more attention. If you haven’t started, get started tomorrow.

Guy Nadivi: All right. And with that final thought, it looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Lee, it’s been fantastic, again, having you back on the podcast and as before, you’ve shared some great insights with us. Thank you so much for joining us today.

Lee Coulter: Thanks Guy. Really good to talk with you.

Guy Nadivi: Lee Coulter, CEO of transformAI and Chair of the IEEE Working Group on Standards in Intelligent Process Automation. Thank you for listening everyone. And remember, don’t hesitate, automate.



Lee Coulter

CEO of transformAI & Chair of the IEEE Working Group on Standards in Intelligent Process Automation

Lee Coulter is a globally recognized thought leader and experienced senior executive with expertise in Intelligent Process Automation, disruptive technology, shared services, BPO, change leadership, customer experience (CX) and practical innovation 

Coulter is currently CEO of transformAI, a hypergrowth automation business. Previously, he was founder and CEO of both Ascension’s globally recognized captive BPO subsidiary as well as Agilify, the nation’s largest IA technology agnostic services business. He brings 30+ years experience in executive leadership positions at companies such as General Electric, AON, Kraft Foods, Ascension and transformAI. 

Lee has published more than a hundred papers, podcasts, and blog posts as a thought leader, is a frequent speaker and leads numerous industry bodies such as serving as Chair of the IEEE Working Group on Standards in Intelligent Automation, the Chief Intelligent Automation Officer of the Shared Services and Outsourcing Network (SSON), member Abundance360, founding member of The Conference Board’s Council on Intelligent Automation, and many others. 

Lee can be reached at: 

Email: lee.coulter@transformAI.com

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

Twitter: https://twitter.com/rleecoulter

Quotes

“…you need to have a fact-based and visible strategy for addressing the fear, the natural fear, that people have that they will not be qualified for the jobs that remain or the new jobs that are created.” 

"And there are lots of examples of companies that are making enormous bets on automation that have made public statements and huge PR announcements about their investments in up-skilling centers and retraining centers. And while I don't necessarily think that that's necessary for everybody, I do think it is essential that you have an explicit conversation and you have proof points that people can look at that say when two thirds of my current role are automated, I can depend on the fact that my friend in another department has gone through the same thing and has a success story to tell on the other side of it." 

“We are in a unique period of multiple exponential technologies converging. And if you look at where each of these technologies are, they're in their 50th and 60th year of exponential doubling of capability.” 

“Unstructured data comes from interactions with humans, with images, with recordings, with documents. And these really determine or have determined heretofore the boundaries of the size of the use case that can be transformed with intelligent automation.” 

About Ayehu

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

GET STARTED WITH AYEHU INTELLIGENT AUTOMATION & ORCHESTRATION  PLATFORM:

News

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

Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages
Episode #29: How Applying Darwin’s Theories To Ai Could Give Enterprises The Ultimate Competitive Advantage
Episode #30: How AIOps Will Hasten The Digital Transformation Of Data Centers
Episode #31: Could Implementing New Learning Models Be Key To Sustaining Competitive Advantages Generated By Digital Transformation?
Episode #32: How To Upscale Automation, And Leave Your Competition Behind

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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

It’s Official! Ayehu is ISO 27001 Certified!

Ayehu is proud to announce that, following the successful completion of a series of rigorous audits, we have officially received ISO 27001 certification. These credentials are internationally recognized and widely accepted among the highest information security standards.

The process to become certified includes strict specifications and requirements that define how a business should process and manage information more securely.

Certification is only awarded to organizations who demonstrate a proven ability to systematically manage information security risks that impact the availability, confidentiality and integrity of company and customer information. The data covered under this umbrella includes, but is not limited to: financial information, intellectual property, employee details and information entrusted to an organization by third parties.

We are pleased to confirm that our internal processes and our unwavering commitment to our clients have met these meticulous standards.

Being ISO 27001 certified is a testament to the fact that Ayehu considers data security to be one of the highest priorities. It also ensures the following:

  • Customer and employee information is rigorously protected
  • Risks and vulnerabilities are continuously assessed, minimized and eliminated
  • An internal culture where every employee prioritizes data security by design
  • Operational excellence, particularly in the areas of IT, HR and information processes
  • Ongoing compliance with the highest standard for information security

For more information on ISO/IEC 27001, please visit https://www.iso.org/isoiec-27001-information-security.html

Want to Innovate? Automate.

The key to innovation lies in the ability to quickly identify and resolve frictions. Easier said than done? Not necessarily – provided you have the right tools in your corner. That’s where the power of AI, machine learning and intelligent automation come into play. By leveraging these technologies, organizations will be better prepared to pinpoint roadblocks and pivot accordingly to unlock new opportunities.  The Process In order to identify issues in workflows, there needs to be a process put in place, and that process must involve mapping out the complete journey of that workflow from start to finish. Hidden within this flow of events is where you’ll find those gaps and imperfections. And chances are, the more detailed the workflow, the greater the number of frictions you will encounter. That being said, the more frictions you find, the greater the opportunities to innovate by solving those issues and streamlining those workflows. Upping the Ante The whole process of laying out a workflow and identifying problems is nothing new. In fact, it’s been employed by top organizations around the world for eons. The problem is, because this process has historically relied on human effort, it’s naturally prone to errors and oversights. Here’s where technology has become a real game-changer.  Not only does AI and automation dramatically speed up the process of monitoring workflows and identifying issues, thereby streamlining processes, but because it’s capable of providing users with improved access to knowledge, it’s empowering users to self-serve. The result is a powerful synergy between human and machine which is enabling enterprises to truly up the ante in virtually every area of operation. Automation = Innovation Thanks to rapid advances, not only are we able to use automation technology to explore and identify frictions, but artificial intelligence and machine learning can also present new and expanding solutions to those issues. This capability is becoming one of the most powerful tools for decision-makers, who no longer have to rely on fallible human suggestions, but can instead choose from recommendations derived from real, quantifiable data.   In fact, unlike human analysts, AI is capable of sifting through mountains upon mountains of raw data and then convert that data into invaluable insights and actions. With intelligent automation, we are able to gain a new understanding of what’s happening in both the physical as well as the digital arena, as well as the context in which these things are occurring. With these insights, we can then take action, whether it be by informing, alerting or closing the loop. If, in the past, we considered the question, “How can we solve problem A for person B,” intelligent automation changes the game by asking, “How can we automate this process and make it more intelligent?” As such, the solutions we’ll develop will ultimately take us beyond the human user to learn what’s standing in our way, predict and plan next steps and incorporate automated actions whenever and wherever it makes sense. By leveraging the power of intelligent automation, we are essentially shifting responsibilities from human to machine.  Putting Ideas into Action It’s easy to write about how AI and intelligent automation has become a game-changer in terms of innovation, but how can organizations actually put this into action? There are two critical questions to ask: •	Given your available data and existing assets, which behaviors, activities, processes or environments could be made more intelligent through automation? •	What, if any, gaps exist within those physical assets and data? Which devices, tools, applications and analytics capabilities could be added into the mix to capture data more effectively and further the goal of automating? When you incorporate intelligence and automation into your processes and operations, you’ll be able to expand your portfolio of ideas and identify newer and better opportunities as a result. And that’s where true innovation can be found.  Ready to get started? Download your free 30-day trial of Ayehu and put the power of AI and intelligent automation to work for you.

The key to innovation lies in the ability to quickly identify and resolve frictions. Easier said than done? Not necessarily – provided you have the right tools in your corner. That’s where the power of AI, machine learning and intelligent automation come into play. By leveraging these technologies, organizations will be better prepared to pinpoint roadblocks and pivot accordingly to unlock new opportunities.

The Process

In order to identify issues in workflows, there needs to be a process put in place, and that process must involve mapping out the complete journey of that workflow from start to finish. Hidden within this flow of events is where you’ll find those gaps and imperfections. And chances are, the more detailed the workflow, the greater the number of frictions you will encounter. That being said, the more frictions you find, the greater the opportunities to innovate by solving those issues and streamlining those workflows.

Upping the Ante

The whole process of laying out a workflow and identifying problems is nothing new. In fact, it’s been employed by top organizations around the world for eons. The problem is, because this process has historically relied on human effort, it’s naturally prone to errors and oversights. Here’s where technology has become a real game-changer.

Not only does AI and automation dramatically speed up the process of monitoring workflows and identifying issues, thereby streamlining processes, but because it’s capable of providing users with improved access to knowledge, it’s empowering users to self-serve. The result is a powerful synergy between human and machine which is enabling enterprises to truly up the ante in virtually every area of operation.

Automation = Innovation

Thanks to rapid advances, not only are we able to use automation technology to explore and identify frictions, but artificial intelligence and machine learning can also present new and expanding solutions to those issues. This capability is becoming one of the most powerful tools for decision-makers, who no longer have to rely on fallible human suggestions, but can instead choose from recommendations derived from real, quantifiable data.  

In fact, unlike human analysts, AI is capable of sifting through mountains upon mountains of raw data and then convert that data into invaluable insights and actions. With intelligent automation, we are able to gain a new understanding of what’s happening in both the physical as well as the digital arena, as well as the context in which these things are occurring. With these insights, we can then take action, whether it be by informing, alerting or closing the loop.

If, in the past, we considered the question, “How can we solve problem A for person B,” intelligent automation changes the game by asking, “How can we automate this process and make it more intelligent?” As such, the solutions we’ll develop will ultimately take us beyond the human user to learn what’s standing in our way, predict and plan next steps and incorporate automated actions whenever and wherever it makes sense. By leveraging the power of intelligent automation, we are essentially shifting responsibilities from human to machine.

Putting Ideas into Action

It’s easy to write about how AI and intelligent automation has become a game-changer in terms of innovation, but how can organizations actually put this into action? There are two critical questions to ask:

  • Given your available data and existing assets, which behaviors, activities, processes or environments could be made more intelligent through automation?
  • What, if any, gaps exist within those physical assets and data? Which devices, tools, applications and analytics capabilities could be added into the mix to capture data more effectively and further the goal of automating?

When you incorporate intelligence and automation into your processes and operations, you’ll be able to expand your portfolio of ideas and identify newer and better opportunities as a result. And that’s where true innovation can be found.

Ready to get started? Download your free 30-day trial of Ayehu and put the power of AI and intelligent automation to work for you.

Episode #32: How To Upscale Automation, And Leave Your Competition Behind – transformAI’s Lee Coulter (Part I)

January 2, 2020    Episodes

Episode #32:  How To Upscale Automation, And Leave Your Competition Behind (Part I)

In today’s episode of Ayehu’s podcast we interview CEO of transformAI & Chair of the IEEE Working Group on Standards in Intelligent Process Automation

Automation in the enterprise has proven efficient, cost effective, & mature enough that early adopters have begun scaling up their deployments to increase returns & amplify their competitive advantage.  The long-term repercussions of this will likely widen the gap between market leaders & laggards.  In the short term, this expansion will raise questions about the best way to approach sweeping organizational change management for what is proving to be one of the most profound changes organizations & their personnel will ever go through. 

In Part I of this 2-part episode, Lee Coulter, CEO of transformAI joins us to examine the issues surrounding how automation deployments should be upscaled, the value propositions most effective in persuading a CEO to move forward on automation, and which senior executive is best-suited to lead an organization’s intelligent automation initiative. 



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 Lee Coulter, CEO of transformAI and IT services provider that leverages the world’s most sophisticated automation solutions to massively scale their clients’ operations and drive business value. Since automation seems to have crossed the chasm from innovators and early adopters to now being rapidly embraced by mainstream organizations, it seems that the issue of scaling up automation is going to be a hot topic going into 2020.  So with that in mind, we’ve asked Lee to come back onto Intelligent Automation Radio, and share his considerable insights with us on scaling up automation. And Lee is also Chair of the IEEE Working Group on Standards in Intelligent Process Automation. So we’re also wanting to tap into the unique insights about automation that he’s gained from this very important industry role, and how it impacts the digital transformations now sweeping across so many organizations.  This will be part one of a two part podcast we’re publishing with Lee. Lee, welcome to Intelligent Automation Radio.

   Lee Coulter:  Thanks so much, Guy. I really appreciate coming back. It’s great to talk to you as always. 

   Guy Nadivi:  Lee, you co-authored the Global Intelligent Automation Market report for the first half of 2019 which was published by the Shared Services and Outsourcing Network, and there was something you said in the intro to that report that caught my attention. After discussing why it was critical to make an internal commitment to change, you pointed to an interesting example when Jack Welch was CEO of GE, and he decided that not only would his company commit to change by going all in on Six Sigma, but that it would be forever transformational.   And you then write, “The case for change was made and Welch then enrolled thousands of leaders across the enterprise into training and built a new operating methodology to govern tens of thousands of projects across the company. It was based on his belief in the power of math and science to identify both the root cause as well as the solution to problems of any kind.”  Now Six Sigma, I think everybody knows, produces an outsized ROI, but so does implementation of intelligent automation. And as a bonus, automation can also eliminate the potential for human error, for mission critical processes, thus mitigating extremely expensive outages. So Lee, with all that in mind, why do you think more CEOs aren’t taking a cue from Jack Welch and announcing a commitment to enterprise-wide automation? 

   Lee Coulter:  It’s a fantastic contextual question, Guy, and it’s an interesting one. And I think it boils down to one of attention and courage. When Jack was convinced of the power of the application of Six Sigma and its inevitable contribution, he made a big bet on it. It was very courageous. And a case for change was a calculation that said that there was about $6 billion worth of waste across the organization.  And he successfully took that out and a little bit more over the course of five years. Intelligent automation is similarly poised, I think we can say successfully that it’s been proven. The technology works, automation stitches together our systems of record and vastly enables the work of our modern digital enterprises today.  So I think that, and I’ll acknowledge that the Gartner adoption curve remains unchanged as long as I’ve been around and I don’t anticipate we’re going to see it change much where you have the innovators and early adopters and early majority and then the late majority and finally the laggards.  And I think that we’re standing in the early majority and so there are organizations that are being deliberate now. The big conversation, to your point, is about scaling. So now we’ve done enough tinkering and we know we’ve thrown some simple, some medium, some hard use cases at the stuff. We’re now able to engage with some unstructured data and do some other pretty clever things.  And it’s now making its way to the CEO’s chair. Intelligent automation typically has a penetration point somewhere in the organization. It’s more often horizontal, meaning it comes in through a function. So supply chains, finance, HR, order to cash, call center, someplace in one of these processes is a typical entry point for intelligent automation. And there’s some spreading, and then it eventually gets to an EVP level of awareness, and finally, it becomes a conversation that is in front of the CEO.  And typically, somebody asks, “What is the total enterprise value? What is the impact that this could have if we made a full blown bet on it.” So I think in a lot of cases, the only CEOs that have been presented with that question and that opportunity are those that are really on the leading edge of the early majority. And this will increasingly become the conversation, whether we call it digital transformation. And I cringe when I use that word the same way I cringe when I have to use the term artificial intelligence. Because the difference between intelligent automation and digital transformation, I wouldn’t know how to describe them.  So I think that the call out here is for CEOs listening or for EVPs that are senior folks that are listening, formulate the question, what is the total enterprise potential and how long would it take you to get there in order to really drive intelligent automation across the enterprise? And keep in mind that the typical three to five years savings potential or G&A, not SG&A but G&A of an enterprise is between 20 and 40% and that’s not my data, that’s data from other analysis. Depends on what industry you’re in.  But every dollar that you take out of G&A is a dollar of net income, and every dollar that you take out of G&A is a dollar you can use to invest into front office activities that change the customer’s experience and then begin to deliver effectiveness gains, which actually drive strategic business value. There’s a lot more to unpack in that, Guy, but I’ll leave it there. 

   Guy Nadivi:  Okay. Now intelligent automation has a lot of value propositions. Cost reduction, error reduction, risk mitigation, et cetera. You just mentioned that intelligent automation is making its way to the CEO’s chair. So Lee, in your experience, which value proposition has been most effective in persuading a CEO to pull a Jack Welch and fully committing to taking the automation plunge? 

   Lee Coulter:  I would say there’s a fairly typical journey map to the way that this comes in. So it comes in and it starts as an efficiency play. So the first question is how much is it going to save me? Right? And the only way you’re going to have that conversation at all is if it’s at least 20% of whatever. If you’re talking to a controller or a CFO or an SVP of supply chain, if you’re going to come in and say, “Hey, I’m going to save you 5%,” it’s not a conversation that’s going to get very far.  But if you’re talking about saving 20% and a typical return on investment for an individual piece of automation is measured in weeks, this is the place where it starts.  And that’s kind of, if you will, the ticket to ride. The table stakes, Guy, is the efficiency piece. There are cases, and I’m familiar with many, but they’re far less common, where intelligent automation started with an opportunity to deliver some sort of an effectiveness gain. So what I mean by that is instead of running purchasing with 40 less people, the objective was to reduce DPO and therefore working capital.  Or instead of reducing the number of contact center agents, the idea was to dramatically improve the quality of the engagement between the agent and the caller by using intelligent automation to perform the mini and micro automations necessary to support that agent during their engagement with the customer. So there are those cases where it starts with effectiveness, but they’re probably nine out of 10, it starts with some sort of savings.  And then once the savings has been proven out, then you take some of that savings, those hours that have been returned to the business and you reinvest them in something that is more strategic in value. That usually takes a year or two before you’re at a point where you’ve got enough hours returned to the business where you’re looking at this and you say, “Wow, without any incremental cost, I could have five or 10 dedicated people working on the next automation use cases that are really strategic in nature.” And that’s when the program begins to really turn a corner and become far more strategic and catch the attention of senior executives. 

   Guy Nadivi:  Lee, the report that you wrote for the Shared Services and Outsourcing Network makes it crystal clear that change management is, “The key differentiator between success and failure,” when it comes to implementing intelligent automation. You also provide very specific recommendations around IT change management, business process change management, and organizational change management. When we last interviewed you a little over a year ago, you emphasized that automation is a business activity, not an IT activity. And that being the case and assuming that a CEO is all in on automation as a driver of digital transformation, who in the C suite would be best suited to lead an organization’s intelligent automation initiative. 

   Lee Coulter:  So Guy, I spent a lot of time on that particular paper and looked back at our own evolution in change management and, as we had been advising customers, the change management required. And it’s interesting, I’ve drawn analogy to the Six Sigma era, the ERP era, the outsourcing of offshoring era. Each of these eras involve what is fundamentally the same activity. Removing an end-to-end piece of work, looking at what can be digitally enabled, optimizing what remains and reimplementing the new work.  And whether you are using your low cost labor from offshore or whether you’re using technology, the work of change management is a business activity. And I use a simple example which usually resonates for people. And that is if you are a frontline operator in an organization and you care about this automation thing and you’ve been reading the newspaper or your feed at all in the last two years, you’re going to automatically assume that there’s going to be cost savings and reductions in labor.  And in the absence of hearing a specific organizational plan that may include re-skilling, up-skilling, that may include taking on new work, higher value work, whatever it might be, you’re going to make up your own story. And so there are aspects of change management that are just true. They’re just the core of human existence and it’s been proven over the last 30 to 50 years as you implement technology and process transformation that you need change management.  So I’ll come back to your question which was who’s in the best position to lead? My general recommendation is that it would be a business lead. So if there is a Chief Operating Officer, I would say that’s probably the best place. In truth, the leadership really needs to come from the CEO for it to be truly effective. because true enterprise scaling means that intelligent automation and the use of digital technologies is truly going to be embraced enterprise-wide.  That’s rarely the case. Typically, again, there’s a penetration point here into a function and there’s a natural progression in which there’s proof in an increasingly large part of the organization, which then motivates other parts of the organization to now embrace automation. Your point that this is not an IT project is something that I’ve said literally hundreds of times.  The use of digital labor, and that’s what intelligent automation turns out to be, it’s literally configurable digital labor, is a business activity. And therefore, the change, the pace of change, all of these things needs to be driven by the business. There are very definite roles, supporting cast roles for audit and controls and for IT and for HR and for other parts of the organization.  But it’s a business activity and if you learn from the lessons of the past, if you look at successful ERP implementation, who did businesses, successful businesses, who did they put in charge of those? They typically chose a senior experienced business leader to be in charge of the ERP implementation. Same thing with continuous improvement.  There was actually a role called Business Quality Lead, which, again, was a business person. So when you put accountability in the business and for business results, it sends the right message and establishes the most predictably accurate structure or correct structure for success in this kind of an effort. 

   Guy Nadivi:  Speaking of senior leadership, you were recently named CEO of transformAI, who I mentioned earlier is an automation service provider whose focus is massively scaling an organization’s intelligent automation operations. Scaling up automation is a hot topic we’re starting to hear about more and more. Does this indicate that the cadence of automation deployments has accelerated to the point that it’s gone mainstream? And if so, Lee, what key characteristics differentiate a successful upscaling from one that is unsuccessful? 

   Lee Coulter:  I think automation was a curiosity, I think, initially. What is this RPA thing? Unattended server-based task automation was … Everything has to start somewhere and that’s where this started and now, when you bring in intelligence services and incredibly sophisticated technologies that we can access via an API, and you can combine attended automation and unattended automation, and you can augment and supplement the way people get their work done and allow them to focus on higher order work.  These are game changers. So we’ve gotten to this point where we as a provider of technologies and services to an organization have reached a point where we know what the key components are for successful scaling. At the same time, the technologies have evolved significantly. I have to tell you, in my role with the IEEE and seeing, just watching over the last four years as the technologies have just advanced in just a remarkable fashion, this is where the game changes.  So we had all of these experiments and these proofs of concepts and we had limited technologies to do that. Now we know what the organizational requirements are, we know what the technological requirements are and we have much greater tooling than we’ve ever had before. And that is why it’s going mainstream.  This this is no longer a “I need to see it proven”. This is more a case of “where should I choose to start”. And those are some of the key characteristics.  Picking the interesting, a couple of data points I’ll share. One is that with very few exceptions, and they’re industrial exceptions. So like BPOs may be an exception to this statement. But in almost no case do intelligent automation programs result in reductions in force. And that’s a very interesting observation.  Another interesting observation in the analysis of stalled programs or failed programs is that a very high correlation, almost a 1.0, I think it was 0.92 or 0.93 correlation between a successful program and one feature is, is there a human being that wears the title of something of automation, right? So are you the VP of Automation, you’re the director of RPA, you’re the SVP of intelligent automation. Somewhere, is there a person that wears the title of being responsible for automation? So if there’s a person, a leader that wears a title, then they will almost certainly have a strategy.  And if they have a strategy, then they have a budget. And if they have a budget, then they have some business outcomes that they’re being held accountable for. And if they have some business outcomes that they’re being held accountable for, then there’s some visible stage on which the achievement or not of those outcomes will become public.  So these characteristics that I just listed out now, it seems maybe counterintuitive, maybe not, but if there’s a VP of automation, then you can assume that VP is going to have a team. That team’s going to have a strategy. There’s going to be a budget, there’s going to be governance, there’s going to be goals and objectives and measures, and there’s going to be some public reconciliation at some point on the enterprise’s investment in all of that in terms of what was achieved.  And so interestingly enough, one of the features or characteristics that differentiates a successful upscaling is literally having somebody in the business who’s accountable for it. Because as I just mentioned, all of those things are connected to that. 

   Guy Nadivi:  This concludes part one of our two part podcast with Lee Coulter, CEO of transformAI and Chair of the IEEE Working Group on Standards in Intelligent Process Automation. Stay tuned for part two as we continue our discussion with Lee on workforce re-skilling and up-skilling, the stall points that can be lethal to scaling automation in an enterprise, and Lee’s predictions for what disruptions we’ll see from intelligent automation over the next few years.   



Lee Coulter

CEO of transformAI & Chair of the IEEE Working Group on Standards in Intelligent Process Automation

Lee Coulter is a globally recognized thought leader and experienced senior executive with expertise in Intelligent Process Automation, disruptive technology, shared services, BPO, change leadership, customer experience (CX) and practical innovation 

Coulter is currently CEO of transformAI, a hypergrowth automation business. Previously, he was founder and CEO of both Ascension’s globally recognized captive BPO subsidiary as well as Agilify, the nation’s largest IA technology agnostic services business. He brings 30+ years experience in executive leadership positions at companies such as General Electric, AON, Kraft Foods, Ascension and transformAI. 

Lee has published more than a hundred papers, podcasts, and blog posts as a thought leader, is a frequent speaker and leads numerous industry bodies such as serving as Chair of the IEEE Working Group on Standards in Intelligent Automation, the Chief Intelligent Automation Officer of the Shared Services and Outsourcing Network (SSON), member Abundance360, founding member of The Conference Board’s Council on Intelligent Automation, and many others. 

Lee can be reached at: 

Email: lee.coulter@transformAI.com

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

Twitter: https://twitter.com/rleecoulter

Quotes

“Intelligent automation typically has a penetration point somewhere in the organization. It's more often horizontal, meaning it comes in through a function. So supply chains, finance, HR, order to cash, call center, someplace in one of these processes is a typical entry point for intelligent automation. And there's some spreading, and then it eventually gets to an EVP level of awareness, and finally, it becomes a conversation that is in front of the CEO.” 

"…once the savings has been proven out, then you take some of that savings, those hours that have been returned to the business and you reinvest them in something that is more strategic in value. That usually takes a year or two before you're at a point where you've got enough hours returned to the business where you're looking at this and you say, "Wow, without any incremental cost, I could have five or 10 dedicated people working on the next automation use cases that are really strategic in nature." And that's when the program begins to really turn a corner and become far more strategic and catch the attention of senior executives." 

“…the Six Sigma era, the ERP era, the outsourcing of offshoring era. Each of these eras involve what is fundamentally the same activity. Removing an end-to-end piece of work, looking at what can be digitally enabled, optimizing what remains and reimplementing the new work.” 

About Ayehu

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

GET STARTED WITH AYEHU INTELLIGENT AUTOMATION & ORCHESTRATION  PLATFORM:

News

Ayehu NG Trial is Now Available
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Ayehu Launches Global Partner Program to Support Increasing Demand for Intelligent Automation
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Ayehu Automation Academy is Now Available

Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages
Episode #29: How Applying Darwin’s Theories To Ai Could Give Enterprises The Ultimate Competitive Advantage
Episode #30: How AIOps Will Hasten The Digital Transformation Of Data Centers
Episode #31: Could Implementing New Learning Models Be Key To Sustaining Competitive Advantages Generated By Digital Transformation?

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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

7 Steps to Creating an Automation Center of Excellence

The Center of Excellence (CoE) for Automation has become a very hot topic these days, moving from distributed organizations that each own several tools and scripting to one vertical center that provides automation solutions across the enterprise.

In response to this growing demand, Ayehu has established an Automation Academy that will help enterprises to transition and build their own CoE, training people to become Automation Specialists / Engineers. This will allow organizations to better prepare for the future (when machines will do almost everything) and help drive efficiencies via automation with a stronger emphasis on innovation.

Building your own CoE for Automation isn’t necessarily as complicated as you may think. In fact, it can be accomplished by implementing just a few strategic steps. Here’s how.

Step 1: Evaluate and Adopt Automation

The first step in the process of establishing a CoE for Automation is to gain adequate understanding of the various challenges, opportunities and benefits of automation. During this process, project management teams may choose to identify certain “quick wins” that can be automated fast and result in immediate return on investment.

Step 2: Define, Document and Set Up the CoE

Having gained a strong understanding of the challenges surrounding adoption of automation as well as the tremendous, quantifiable opportunities it presents, the next step is actually establishing your Center of Excellence for Automation. This involves selecting the appropriate core team members as well as evangelists who will assist in spreading awareness and advocate for the benefits of automation.

Keep in mind that the ideal core team for a CoE demonstrates a broad spectrum of skill sets. For instance, you’ll need someone who can assess the impact and document the processes, someone who can handle the implementation and integration process and someone else who can monitor and test the automation.

Step 3: Establish Systems and Infrastructure

Your CoE for Automation will only be as effective as the technological foundation upon which it is built. Making wise choices upfront about the systems and infrastructure you establish will set the stage for rapid growth and also help to prevent potential issues from occurring down the road. Invest in enterprise-class automation and architecture that includes robust features. Create and document best practices with a focus on automated processes that are consistent, efficient, accurate and auditable.

Step 4: Train, Educate and Reskill

While automation will inevitably eliminate some jobs, there are opportunities to train and reskill people for new, next generation roles, such as Automation Engineers. Reskilling and redeploying back to work will ultimately create higher value for the organization, its clients and for the employees themselves. Look for training options that are specific to CoE development, like Ayehu Automation Academy.

Step 5: Sustain and Scale

Once your CoE is officially established, the next phase should involve aligning the automation strategy with the strategic objectives of the organization. This typically involves scaling the approach to make it broader. For instance, while the initial goal of automation might have been to reduce costs, the scope should eventually evolve to include such larger goals as creating stronger customer loyalty or driving greater agility.

The entire CoE needs to work on firming a matured process so it can become agile enough to respond to demand and maximize efficiency. This process should have a definition of how the organization should approach the CoE, how the CoE should evaluate and prioritize these requests, how it should develop its internal design to production processes, etc.

Finally, the core CoE team should specifically include analysts who can continuously identify automation opportunities, translate business needs to IT processes, determine potential ROI and create the logic steps necessary for the automation engineers to build and implement the processes. Remember – a CoE isn’t stagnant. It’s something that must change, evolve and improve as time goes by.

Step 6: Incorporate Automation into the Culture of the Enterprise

Ultimately, automation should become a complement of continuous process improvement for the entire organization. The last step of building a CoE for Automation involves changing the overall business mindset to embrace the opportunity automation presents to change and improve how it operates.

Creating the CoE without making a cultural change in the organization simply will not work. The organization (the people) must change their behavior and think about automation as opportunity to live better, to focus on more important things and be freed up for innovation. Embracing automation will allow the CoE to become relevant to an organization that wants to change and automate as much as possible.

Keep in mind that this phase can take a good deal of time to complete. You’ll know you’ve achieved success once automation becomes embedded in every department and function throughout the enterprise.

Step 7: Market the CoE

Once the CoE for Automation is successfully established and the necessary cultural shift has been set in motion, it’s time to start promoting the CoE to outside to end clients. Any client-facing employee should be prepared to sell the innovation and success stories of automation. This will create demand generation and fulfillment and help the organization achieve maximum competitive advantage.

This is clearly a high-level overview of the CoE process, but it should at least provide a framework upon which to build. If you’re considering making a move in this direction, we encourage you to take advantage of our resources and expertise by allowing us to assist you with developing and establishing your Center of Excellence.

Why go it alone when you can rely on a team of experts who can help you every step of the way? To learn more or get started, contact Ayehu today.

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