EPISODE #33: How to Upscale Automation, and Leave Your Competition Behind (PART II)

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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