Episode 6 – Humalogy and The Future of Work

Episode 6 – Humalogy and The Future of Work

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Today’s guest is Scott Klososky, the founding partner of Future Point of View and creator of Humalogy. Host Jon Knisley, long-time technologist helping companies win the market with emerging AI technologies, talks with Scott about the transition to automation, and working alongside AI and automation. They also talk about how the pandemic has disrupted Scott’s process (as a consultant), and how humalogy principles should be utilized because of the shift to virtual work environments.

For years the promise of technology has been the ability to produce a more productive workforce. The challenge however is that while technology may improve productivity and efficiency by automating routine tasks, today it does not embody or transfer the human conditions necessary for building interpersonal relationships. 

Humanizing technology is still in its infancy therefore when trying to maximize efficiency and develop connectedness, there’s a careful balance that must be struck. To that end, Humalogy examines blending available technologies with human effort to maximize performance and potential. 

Scott Klososky is an international speaker, futurist, author of four books, as well as an entrepreneur who has started five successful companies.

Talking Points:

  • “Many is the waves but one is the sea”: working alongside AI 
  • Automation and the public
  • Processes, and the golden triangle of people, framework and technology
  • Leaders should utilize the technology that is already available
  • How Scott uses the concept of humalogy since the pandemic 
  • Working alongside technology and how tech is changing because of the pandemic
  • The internal impact to staff that needs to be reskilled, retrained for new tech


The Digital Optimist


In Machines We Trust

Future Thinkers

The Trailblazers Podcast with Walter Isaacson


If you enjoyed this episode, subscribe and check out our series at fortressiq.com/podcast. Thanks for joining us today on hello, Human.

Full Episode Transcript:

Jon: Scott Klososky, the founding partner at Future Point of View joined us today at the hello, Human podcast where we discuss the latest topics in artificial intelligence and how it’s being applied in the real world. 

I’m Jon Knisley, the host of hello, Human, and a long time technologist helping companies win in the market with emerging AI technologies. A big thanks to FortressIQ for sponsoring the program, and be sure to hit the subscribe button wherever you listen to podcasts. 

In this episode, we’re going to explore Humalogy and the future of work. Since the days of the industrial revolution, technology has been positioned for its ability to create a more productive workforce. Ever since—while tech may improve productivity and efficiency, too often it fails to embody the human conditions necessary for creating positive experiences. Humalogy examines blending available technologies with the human effort to maximize performance and potential. 

Scott is the creator of Humalogy, so it promises to be an interesting conversation. In addition to his consulting work with Future Point of View, he is also an international speaker, futurist, author of four books, as well as an entrepreneur who has started four, five successful companies. I should also note that I’ve known Scott for close to 30 years now, and that is a scary number to be sure because it ages me considerably. He’s been a great friend, boss, and mentor over the years. 

Welcome to the program, Scott. To get us started, maybe you can share a nugget from your interesting and varied career for our listeners to give them some context and perspective.

Scott: Sure. First of all, I got to say I love being in a podcast called hello, Human that is going to be talking a lot about AI. I love the name. I think the nugget that I would give that might help people know me a little bit is when you said I developed Humalogy, we actually sat at a whiteboard and developed this concept probably 10 years ago. We developed it because there wasn’t a word to describe very eloquently the integration of humans and technology, nor was there a way to measure it.

I was really interested in how are we going to have a conversation around it’s not technology or human, it’s technology and human integrated. How are you even going to talk about that? How are you even going to be able to have a language to say that a process is this much human or this much technology?

The reason that I was interested in this was to try to help the world make the transition between everything being done by hand to a world where machines will do a lot of the tasks for us. Glad that we came up with the concept. The motivation was just to be able to help us make the transition that we are flat in the middle of right now.

Jon: That’s great. Thanks for sharing and it’s probably a topic for another conversation, but Humalogy is not the only word that we’ve invented over the years, as you know. I grew up in New England on the coast, and we’ve got this local saying that many as the waves, but one as the sea. I think that embodies our relationship. In many ways, we are very different but at the core—especially around our outlook on technology and society—were of that same mindset.

When people ask about the impact of technology on humanity and society, generally, they fall into these two camps. You either have this Hollywood dystopian view that the robots are going to take over and destroy the universe, or you’ve got the more utopian view that technology will make life better and lead to a more prosperous future. You’ve got a podcast of your own called The Digital Optimist so I can imagine where you stand on this, but is there a darker side to technology that we need to worry about?

Scott: There is, and for somebody who spends a lot of time looking into the future, I also spend a ton of time looking at the past. I love the concept or the idea that the past doesn’t necessarily dictate or tell us exactly what the future will be, but it rhymes. The future rhymes with the past. I love that concept.

When I look at humans, humans for thousands of years, we fear things we don’t know. We fear something that is new and as such, it is with technology and at many levels, not just AI. They feared robots when robots first started coming out. We fear laptops. My mother, to this day, fears her laptop. She thinks it’s the most complicated device in the world that is meant to frustrate her. 

Your question about yes, I am an optimist about ultimately what happens with the blend of technology in humans, but there is a darker side, of course. Just because I’m an optimist doesn’t mean there aren’t also problems. I am often saying to people that when I look at the future of AI, robotics, and machine intelligence, it’s 55% good and 45% bad. 

The good wins out, but there are some darker, painful aspects that we’re going to see over time. We could spend the next 30 minutes talking about what they are, but certainly, it’s going to be the misuse of machine intelligence. It’s going to be the bias that gets built into machine intelligence. It’s going to be human beings losing skills because machines do the skills for us, and we forget how to do the skills. That kind of thing is already happening to us. There’s a list of things that are a bit negative. I just believe that the list that is positive is slightly larger.

Jon: I like that breakdown of good versus bad. I also think that sometimes, the bad is an unintended consequence. It’s because the technologist didn’t think through the AI completely, and it’s not because of some evil nature of the person. I mean, that’s obviously going to be a part of it, but again, in general, we like to see the good in people. People have positive intentions, and sometimes those challenges that come up are those unintended consequences, then you can adjust those as well.

To get a better handle on this integration between technology and humanity, you developed this concept of Humalogy to really describe that appropriate blend of humans and technology to optimize those targeted outcomes and amplify performance. You’ve also got a framework for determining what that amount of technology is appropriate. 

First off, can you just give us a primer on Humalogy? How did you come up with the concept? How do you define it? How it gets measured? How it gets applied? Just some background to give the audience an understanding of the concepts.

Scott: Sure and we’ll keep this simple. If you think about a process and much of life is based around processes, you could look at any process and there’s a continuum that at one end of the continuum is a process that is completely done by hand. We give that a score of H5. And then at the other end would be a process completely done by technology. Knowing that we have to build the technology, but let’s just say the process is completely run by some type of technology, and we would give that a number of T5. 

We build a continuum. There’s zero, which would be something that’s right in the middle that’s half human half machine to get it done. Then you have H1, H2, H3, H4, H5, and then T1, T2, T3, T4, T5. When you look at that continuum, it now gives you a vocabulary to be able to say, okay, if we look at a process like hiring a person in HR, how much of that today is based on technology, and how much of hiring that person is based on humans? It is a mixture, and it is a different mixture. It’s a different recipe for different companies. 

For some companies, the hiring process like Amazon might be T3—very little human, but some. Then there might be a law firm, and their hiring process is an H3—there’s some technology involved, but it’s predominantly human. That’s what the term when we say Humalogy, we’re talking about that scale. We’re talking about a continuum that is an integration of humans and technology. 

You mentioned also optimizing that. Optimizing where a company should be or where a process should depend completely on the type of company, type of process, and type of culture. There are no fixed right or wrong answers, but there is what is best, what is efficient, and what is most along with the business plan for the company. 

There is a proper place, as you can imagine. If you have a process and you are H3 and you have competitors and they are at T2, they’re much more efficient, much higher throughput, much higher quality because they’ve integrated technology. You may be at the wrong place on the Humalogy scale and it might be bad for your organization.

Jon: To me, one of the most beneficial takeaways is that Homalogy can address more than the typical automation use cases. In today’s environment especially, it’s easy to look at everything and see an opportunity to automate, and that’s the soul drive of the technology. What do you see as the major use cases beyond automation where your clients are applying Humalogy? Can you provide any real-world examples?

Scott: Yeah, and it’s a good point because automation is powerful. Automation is something that organizations will be deep into, especially driven by the pandemic now. There will be even more of a hunger for automation over the next few years because when you have people working from home or in a distributed work environment, you really need automated processes and systems. 

The best example that I can give you—for instance of a good change. There was a client that we had in the middle of the pandemic who had a payable system. The way their payable systems work was they got invoices from vendors. The invoices could come in over fax, they could come in over email, or they could be mailed. They would accept invoices any way that anybody wanted to send them. 

They had staff who would type in the invoices and then it was in their payroll system. Then they would have it reviewed by a payables leader and say this is okay to pay, then they would print out checks, then they would take the checks over to the CFO, and the CFO would sign the checks. That’s a fairly standard right in the middle of Humalogy—part technology, part human—to get payables paid. 

In the middle of the pandemic, they couldn’t pay payables because they work together. It wasn’t as easy to get the invoices, harder to get them keyed in. You couldn’t print checks out. You couldn’t have the CFO sign them. In three weeks, from the time that we went to the emergency work from home, they went to an online payable system. They completely automated the payables program. Obviously, they will never go back now. 

You have to deal with the reality that there were two or three people that were payables clerks who now don’t have a job. Those people can be upskilled. They can be moved somewhere else in the organization. 

That’s the kind of thing that we are seeing, which is a shift—the constant shifting that automation does. What we try to do is look at every process and say, all right, is it appropriate for it to move from H2 to T2? Or is that something that doesn’t fit with this business because the customers don’t want a technology interface? There are cases where it’s not about moving from H to T. It’s about staying at the right place in H and optimizing that. I think the misnomer is everybody should be moving from H to T.

Jon: Along those lines, you touched on the impact internally to the staff that needs to be reskilled, retrained. How do you deal with the situation where the company realizes it needs to go from H to T on the spectrum, but their customers may not be ready to go from H to T? Is that a decision the company drives, obviously, but what are the factors that go into determining that shift?

Scott: As you know, I am right now visiting my 82-year-old mother. I spent my last two days dealing with this topic. The companies are switching to full automation, and I’ll give you an example. I’ll give you a couple. 

One was there’s a magazine that she absolutely loves out of England. She resubscribed and they had changed it over to a digital magazine. They wanted her to download a PDF and read a PDF. She lost her mind. I am not doing that. I want a paper magazine. If I can’t have the paper magazine, then cancel my subscription. That’s a customer who isn’t going to accept your automation and digitization. 

Then there was the whole health care issue with my mom where all the health care providers and pharmacy people wanted to set up online sites and use their online locations to manage her health care information and all her prescriptions. She’s like, no, I’m not doing that. Give me a phone number and I’ll call somebody, talk to them. 

They started taking away the phone numbers, they don’t answer the phones, or they get her stuck in an IVR. These are cases where you’ve got to look at your customer and say, hey, automation is fantastic, but not if your customer base is 80-year-old people who don’t feel comfortable with technology.

Jon: Yeah, I can relate. As much as I love talking to my dad, I dread when the call comes in and it is the computer help desk call. The browser is not working or the website won’t load. I know I’m in for a good 45-minute conversation at a minimum. 

You mentioned the process word a couple of times. As a consultant, you’re taught to fall back to that golden triangle of people, process, and technology. I think in many ways, Humalogy really takes that framework a step further. Obviously, it explores the people and the technology dimension, but the process dimension is also front and center with Humalogy because it dictates how those other two relate to each other. 

I’ve been arguing recently that a big part of the challenge with transformation is that the process element has been ignored in recent years. There was a number from McKinsey that I saw last month, that was I think only 14%, 15% of companies have seen sustained and material performance improvements through all their efforts. 

Another one I saw just last week was less than 1% of companies have enough process understanding to fully leverage the digital solutions that they have in place. There’s been too much reliance on technology in my mind. Do you see this in your work?

Scott: Yeah, I absolutely do. I do believe in the triangle of people, technology, and processes. I just think those are not silos, first of all. I think those are integrations, and I agree the integrations have not done well. I see this is just a maturity process. This is a digital maturity process because we are farther ahead with the tools than we are with applying the tools. I think in some ways, if I step way back, that’s natural, that’s to be expected. 

I do think that leaders—any of you who are listening right now who are leaders, one of the most beneficial thing a leader can do is to say, hey, let’s fully exploit what’s already out there. A lot of it is taking that Humalogy view of a process because I agree with you. We have the technology, we have people, but what we don’t do is rebuild the processes fast enough or well enough. That’s really where the gap is. 

There is no excuse in that we do have the technology. It can do wonderful things. We do have people. They can do wonderful things. All we are lacking is the focus on automating the processes in the appropriate way. 

Think about the example I gave you about payables. That company could have automated those payables in the way I described any time in the last three years, but why hadn’t they? They were forced to do it in the pandemic. You have to ask yourself, the technology existed, why hadn’t they moved their payables? Well, because whoever was running the accounting department was perfectly happy with the H2 or whatever the way that they were doing payables, 

Jon: I was just going to say—from my time at FPOV—I think one of the things I’ve taken away and continue to stress is you can’t undertake a major, complex change program without first being able to map your processes, map your experiences, and map your technology. Those three areas of understanding that the current state is so critical to successfully getting to that magical future state that everybody is trying to get to. 

As a professional speaker, I imagine that your work has been disrupted by the pandemic, and you’ll be excited to ultimately get back on the road. Hopefully sooner rather than later. How have you applied the principles of Humalogy to make adjustments to your work in that area? You’ve always worked hard to integrate technology into your events, but working virtually—I must imagine—has accelerated some of that technology adoption.

Scott: It has. Thankfully, we were already experimenting quite a bit with virtual speeches, keynotes, and things like that because I was getting asked to do a lot of international work, and we couldn’t always fly over to other countries, so I kind of had a head start. 

But in March, April, May, we really invested in new technologies, trying new ways to deliver content. Let’s try to break down the 45-minute keynote or the 1-hour keynote with very little interactivity. Let’s throw out the rules on (what I’ll just say as a) virtual interaction. Let’s try to create whole new ways to virtually interact with an audience that’s a lot better than just the traditional keynote.

The speaking industry got decimated in 2020. I gave more speeches than I ever have and experimented the whole year with interesting ways to deliver content. A lot of the experiments worked really well. Speaking, it’ll go someday in 2021. It’ll go back or it will go to some kind of integrated format. We’ll go back to more in person, but it’ll be a hybrid, it’ll be a mixture. I’m comfortable along the way if it remains highly technology, virtual-oriented, we will continue to try to pioneer cool ways to deliver content. If it goes back to being quite a bit in person, great, I love that as well.

Jon: The virtual events are obviously a challenge for everybody—participants and speakers. People tend to joke at me a little bit in company meetings because I tend to have my camera on, and that’s just the way that I’ve been taught and brought up. If you’re going to be on there and you’re going to be part of that conversation in this new world that we’re living in, you’ve got to use the technology to have that connection with people. 

If you treat it just like a conference call or how you put it on mute and you’re doing 15 other things, you’re not actively involved in that conversation. I think that’s where companies need to come in sometimes and have some governance involved in what’s the appropriate use of the technology and how you should engage with it to get the most value out of it. Would you agree with that assessment, or do you have a different take on that?

Scott: Absolutely. We tell clients the same thing. You have to have rules for how you work from home. You have to have rules for how you do virtual meetings. It’s not how do you handle a sales call? How do you handle a meeting with just two people internal? How do you handle a meeting with four people external? You have to look at every virtual interaction, then you have to design it.

Organizations are way behind the curve on this. They got Zoom, they got Teams, and they got whatever. They figured out how to have virtual meetings and then they stopped. That’s crazy. You’ve got to step back now. What’s every kind of virtual interaction you have and you need to design it. When I say design it, I agree with you. I think when people have internal staff meetings, and they don’t turn their videos on, that’s rude. It’s just rude.

If that offends anybody, I apologize, but I mean it. Because in a world where we’re distributed, you’ve got to build relationships in some way. Humans are designed to want to be able to see each other, see the body language. I think there’s a lot of leaders that just allow people to have a black screen who say, I didn’t want to get dressed up. I didn’t want to put my makeup on, whatever the excuse is. I find it to just be, honestly, rude when people don’t want to turn their video on.

Jon: Yeah, and I think that goes a little bit back to our previous point around the people, process, and technology. It’s ignoring the process piece of it. You’ve gotten this technology, it’s in place. It’s what I’ve seen as a consultant in my past. Most times, companies have made the right technology decisions. They’re just not necessarily using the technology fully or using it as it’s been intended. That ultimately ends up being a process issue. 

Scott: Absolutely. 

Jon: To bring this conversation full circle, I want to shift quickly to the future of work. The Brooklyn Institution cited some research that over the last three recessions, over the last 30 years, 80% of job losses—just a massive number—took place in what they call routine automatable occupations. Essentially meaning that the jobs accounted for all losses and the crisis all came down to automation. How do you respond to that data point using the Humalogy lens?

Scott: I think about this a lot. Machines are going to replace three things that we do—highly repeatable tasks, which is what you just mentioned. Highly repeatable tasks that are pretty consistent and a machine can replace them. They’re going to replace very complicated tasks that humans don’t even have the ability to do on their own. 

For example, predicting the weather. No human is going to take in data in their brain from a thousand different weather stations and then do all the math to figure out what the weather is going to be. That’s going to be an AI and it is now. A very complicated decision, some health care decisions. Those are going to be made by machines.

And then the last is anything that could keep somebody safe. Where a robot or a machine can do some tasks that keep people safe. That’s the formula. Machines are going to replace things that are highly repeatable and simple, very complicated so no human can really make great decisions or tasks that make somebody unsafe. 

Now, that represents a large amount of jobs. Over the next decade, there will be a big transition. If you want to know the future of work in my mind, we will have a decade or two of transition. It’ll be painful because some of the people who get knocked out of work are going to have to upskill or reskill. They’re not going to like it, some won’t do it. 

I do not see this collapsing the economy. I do not see this causing a lot of problems for humanity. I actually see it the other way. That this is going to free humans to do work that is more interesting, more fulfilling, and safer, but the transition will be rocky.

Jon: Scott, I think that’s great insight and a great point to end on. To recap today’s conversation with Scott Klososky, the founding partner at the technology consultancy Future Point of View, as well as an international speaker and entrepreneur. 

Humanizing technology is still in its infancy, so when trying to maximize efficiency and develop connectedness, there’s that careful balance that must be struck. To that end, Humalogy examines blending the available technologies with the human effort to maximize performance and potential. It’s a great framework to consider as a solution to the miserable success rates of transformation programs that we talked about by bringing the focus a bit back to process awareness. 

Thank you, Scott, for joining me today. I want to give you an opportunity to make any closing comments or provide any final insights, but I also have a final question for you as well. As you know, I’m a bit of an information junkie and always looking for the latest and greatest resources. My question to you is, what resource—whether it’s a website, a newsletter, a podcast, or whatever it may be—do you rely on most to be successful and knowledgeable in your role?

Scott: Well, I love podcasts. I listen to them all the time when I’m driving, running, in an airport. I would give you three podcasts. One is called Singularity.FM. A great podcast about AI, transhumanism, ethics, philosophy, and all those kinds of things mixed together. 

I would probably also say there’s a new one out called In Machines We Trust, which I believe is MIT talking about AI and where it’s headed. And then there’s a podcast called Future Thinkers, which is not always technology-based. Sometimes it is, but a lot of future thinkers. It’s just on philosophy and what’s going on in the world.

To me, it’s a combination, Humalogy. I don’t want podcasts that are just about technology, and I don’t want podcasts that are just about humans. I want podcasts that mix up those topics. If you listen to each of those three, it’s a good river of information for you.

Jon: That’s awesome, and those are three new ones for me. I’ll be sure to put them in the show notes for today. I think my resource for this episode that I can’t miss is I’ll also go the podcast route, the Trailblazers podcast with Walter Isaacson. Each episode is about 30 minutes, and he really explores that untold story behind some of the world’s biggest digital disruptions, the people that really make it happen, and what they learned from it. Recent topics have been everything from genomics and esports to robotics and mattresses, of all things. Really pretty fascinating stories about different topics.

That’s a wrap on today’s show. Thank you, Scott, for joining me and for FortressIQ’s sponsorship. I’m Jon Knisely, and this has been hello, Human.

Episode 5 – Understand Today, Automate for Tomorrow

Episode 5 – Understand Today, Automate for Tomorrow

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Today’s guests are Craig Le Clair, Vice President and Principal Analyst at Forrester Research, and Stephen Siciliano, Partner PM Director at Microsoft for Power Automate. In this episode, Craig, Stephen, and I explore how companies today are under near-constant pressure to accelerate growth, efficiency, and ROI. Effectively implementing intelligent automation at scale is crucial for success in the enterprise, but this remains a challenge with large organizations because of a lack of resources to streamline business process discovery, gather accurate data, and then easily kick off automated workflows. 

Maintaining continuity and managing through change is an existential challenge, as business processes experience enormous demand spikes. By using process mining to more effectively orchestrate process automation, companies can address both near- and longer-term operational challenges. 

Craig serves enterprise architecture and business process professionals. He is an internationally recognized expert in automation, AI, and the future of work. His technology coverage areas include robotic process automation, AI solutions in financial services, and the potential workforce disruption due to these technologies. His 2019 book, Invisible Robots in the Quiet of the Night, has been met with wide acclaim. 

Stephen is the Partner PM Director at Microsoft for Power Automate. Power Automate is a service for automating workflow across the growing number of apps and SaaS services that business users rely on.

Talking Points:

  • Accelerated Digital Transformation 
  • The Workforce of the Future 
  • Human-Machine Interaction 
  • The Process Gap 


Robots in the Quiet of the Night 

Power Automate 


If you enjoyed this episode, subscribe and check out our series at fortressiq.com/podcast. Thanks for joining us today on hello, Human.

Full Episode Transcript:

Jon: Craig Le Clair, vice president and principal analyst at Forrester Research, and Stephen Siciliano, the partner PM director at Microsoft for Power Automate, join us today on the hello, Human podcast where we discuss the latest topics in artificial intelligence, and how it’s being applied in the real world. 

I’m Jon Knisley, the host of hello, Human, and a long time technologist working with companies to leverage first in class IT applications to win in the market. A big thanks to FortressIQ for sponsoring the program, and be sure to hit that subscribe button wherever you listen to podcasts.

In this episode, we’re going to explore how companies today are under near-constant pressure to accelerate growth, efficiency, and ROI. Effectively implementing intelligent automation at scale is crucial for success in the enterprise, but this remains a challenge with large organizations because of the lack of resources to streamline business process discovery, gather accurate data, and then easily kick off automated workflows. With these two true industry leaders joining us today, it promises to be an interesting and lively discussion.

Craig wrote a great book last year on the impact of AI and automation on the workforce. It’s called the Invisible Robots in the Quiet of the Night. I would encourage you to track it down. I’ll also put a link to it in the show notes. Stephen’s work deserves some congratulations as well, as the Power Automate platform has recently received significant recognition from the analyst community, which is just incredible given it just went into GA this spring. 

For today’s episode, Craig is going to start us off and give us a briefing on some of his new, not yet published research around the autonomous enterprise, which is fascinating. Then Stephen and I will join the conversation to discuss with Craig how technology in the post-pandemic environment is going to shape the digital workforce of the future. 

Welcome to the hello, Human podcast, Craig. I’m excited to hear the analyst viewpoint today since you have the benefit of talking to a diverse group of technology leaders and strategists and then get to work your magic and meld all that thinking together into coherent trends and outcomes. 

Craig: Yeah, thank you everybody for joining and also for that great introduction. Understanding today, Automate for tomorrow—that’s what we’re going to talk about. Thanks for the plug for the book. It’s doing quite well so I’m really thrilled about that. But let me get into the content here. I’ve been with Forrester for about 15 years. I’ve used that collective knowledge to try to bring all this automation together and what it means for our society and for the future of work.

Now, what we’ve seen in the last six months—we’ve all lived through it so I don’t need to talk about the effect, but I will in one small context—and that is this rate of acceleration in digital transformation. As analysts, we’ve been talking about transforming digitally since the mobile explosion in, say 2010, 2011. The iPhone had come out, and all of a sudden everyone needed a mobile app. The consumer had power in their pocket that the companies couldn’t deal with because they had gaps in their digital capabilities. And of course, we had disruptive companies with the sharing economy, mostly coming in and disrupting traditional businesses whether it’s the taxi cab business, publishing business, and so forth. 

Digital transformation was a topic and a strong initiative of the company, but very slow progress until we hit the pandemic, and now we’ve all developed these digital muscles. We’ve learned how to shop online. Obviously, we’re all working remotely, those of us fortunate enough to be able to. There’s been this surge in transformation and a search in intelligent automation, which I’ll describe briefly what that is. But it’s really been unprecedented, the amount of energy and investment that’s going into what I call practical automation. This would be task automation RPA, but also the more pragmatic AI building blocks. That combination is what we refer to as intelligent automation. Companies are basically redoing their road map for projects along the lines of this two-by-two.

Like all two-by-twos, it’s the upper right that is the more prized real estate. What you’re seeing there, what we call the acceleration zone, is automation that’s being applied in these areas, doing remote work. Our forecast,  as I’ll show you in a minute, is tens of millions that will remain working from home post-pandemic whenever that golden period is. Automation is needed to support them in different ways. A part of that is text analytics, which is being applied to paper processes, to documents, and emails, and forms. You can’t go to the office and shuffle papers anymore.

Remote business is another area, the number of banks, for example, that don’t have mobile deposits is pretty high like 30%, 40%. There is still a significant amount of business that depends on the face-to-face type of interaction, which is very difficult under the current conditions, so the answer is automation. The projects that companies had which might have been balanced between more tactical automation and more transformative AI-based business transformation, that’s the lower left, those projects are losing momentum. That emerging technology team that might have been working with open source tools, and trying to figure out how to instantiate the next-generation business algorithmically is now being de-emphasized for this more practical automation that has to occur, backed by the recession, as well as by the pandemic.

This is a rejiggered road map, if you will, that the buyers that we talked to, this is how they’re thinking about the world going forward. This is a bold attempt to try to define what the workforce looks like post-pandemic and where it’s been. Now, we call it the golden age of work. But for many years, we had relatively stable employment for a large percentage of the population. Automation expanded jobs rather than disrupted them more. You had an automatic nail gun, but the carpenter was still deciding where to put the nails. It just made that job easier. 

You had stronger labor unions, you had stronger and more stable middle-class benefits and income. But what you’ve moved to is a world where you’re going to see, with the recession and with automation, automation deficits, or job losses start to pile up, traditional safety nets are declining. You’re seeing social unrest. A lot of what we’re seeing on the streets today is this combination of factors. A part of it is this loss of hope for middle-class lives to be expanded the way they were in the golden age of work. There’s a lot of frustration underlying a lot of society right now and a lot of it has to do with work, a lot of it has to do with the pandemic, a lot of it has to do with anxiety-related automation.

The widening and exposing skill-gap that workers see when they go into the workplace and all of a sudden, the technology they’re dealing with is harder to understand, it’s maybe evolving at a faster rate than they are. These are all aspects of the new workplace. Tactically, digital elites spread far and wide. We’re going to see a significant growth in the talent economy that can be a human cloud if you will. We’re going to see a lot of workers are made at home, and so forth. Organizations will flatten and education is going to be rethought—the value of traditional education—with the pace of automation, and this development is going to be exposed as not being the most adequate way to do it. We’re going to need the private sector to step up and push education at work and lifetime learning.

A bit of a view of where we’re at this nexus is really an important point. I won’t go through this table in detail, but there is a lot of discussion of where we were pre-pandemic in terms of work from home and that’s that second column from the left. What we’ve done here is predicted based on the type of skills that you have in your work. On the left, you see these personas. Personas are essentially generic work categories where the occupations, of which there are 980 that the US Bureau of Labor Statistics tracks, we’ve mapped them into these smaller sets of generic categories based on these skills, thinking that automation will affect these skills in such a way that you can think of your workforce in these more simple and more generic terms.

If you’re a human touch worker, such as a massage therapist, you’re going to have this movement from 3% pre-pandemic to 8% where you’ll be working from home. You go down these areas and you see the most advanced knowledge worker that cross-domain. We think 35% of that population will remain working at home post-pandemic. All of this means it’s a sea change in how we’re working, where we’re working, that’s going to affect our lives, and basically, how we get work done. That’s a brief view of that.

Now, when you look at automation, this is a model that’s coming out and this is actually not published yet, but we have divided work in terms of trying to look at it in terms of how humans and machines interact. This is the basis of the pyramid. At pattern one, the software is completely controlling the process. We’ve been doing that for many decades. This workflow, digital process automation, deterministic, human-machine interaction that is defined and fully articulated by a task map, may be built with a process orchestration engine. 

The employee-driven is the interesting one for Fortress and for the intelligent automation market because here you see humans controlling the automation, firing off an automation. Basically giving instructions to a bot to go off and do something. This is an area that is going to accelerate in the next few years. It’s been accelerating right now due to the pandemic and I’ll blow that out a little bit for you. 

Semi-autonomous, you have humans and machines as peers. Your machine makes a decision or makes a set of recommendations, and provides them to the human. The human will make a decision based on that. You start to move at levels three and four patterns into this non-deterministic world based on AI, which basically is using machine learning and refreshed data. Some of that data can reformat the algorithms within the model. You get into the issues around explainability, you get into issues of predictability, you get into issues of emasculation of the human worker.

If you look at these four patterns, of course, four, completely autonomous is where the machine does everything. The human is not really in the loop in that sense, but the key is that to understand where automation is going in the future and how it can be best deployed in your organization, you need process technology to understand how the humans interact with the machine—what is the specific interaction, what are the implications of that interaction. You need to think about the pattern level that you’re at. This is not a progression. You can have a level one pattern that’s interacting with a level four pattern. When you recognize the characteristics of the pattern, you need to understand the right level of process understanding and process analytics that you need to drive the best result.

Now, as you move up those levels, you need to focus more on some of these aspects if you look at these seven aspects. These all get to be more important as you move from pattern one to pattern two, pattern three, and pattern four. Obviously, there is no real algorithm in level one. Level one is just a task-to-task type of deterministic workflow orchestration. But as you start to move to that level four that’s a closed-loop pattern, the algorithm has to be perfect. Otherwise, you’re going to have a bad result and this is where you need those kinds of checkpoints in the development of the algorithm, and testing it, and looking for bias, and so forth.

As you move to higher patterns, you need specific steps for bot mastery—for how humans will control the bot, how they will train the bot, how the bot will inform the human about ways that the human can act better. There is an interaction with humans from a learning standpoint with the machine. You need design thinking and digital worker analytics, and this is what we talked about in the last slide. The more you start looking at level two patterns, in particular, level three patterns, level four patterns, you have to have a way to study how humans are interacting with the machine.

You need code development because a lot of the more sophisticated algorithms have to stem from knowledge of the business and intimacy with the business. You need business, but also because you’re dealing with more advanced technology, you’re dealing with machine learning, you’re dealing with text analytics, you’re dealing with conversational intelligence, you need strong and deep professional development and data science capabilities. You need this converged development environment to collaborative low-code and deep development teams working together. That’s very important at higher-level patterns. And of course, it’s all about data, higher quality data is needed. The influence of data in an ongoing refresh, the basis of making decisions, that are predictive is used for decision management in the upper-level patterns. You don’t have that at the lower level patterns and of course, the governance for the reasons I cited. 

Employee anxiety as we talked about in the future work model, is very important. We’re going to see that we need to start designing these systems at levels three and four, where we understand what the effect on the human is of different patterns within the machine work. We’re just seeing that understanding the anxiety due to skill gaps, understanding that being able to turn to monitor on and off might be critical in this pattern, but not in this pattern. That’s going to be an area of greater discussion going forward in an area of greater research.

All of these patterns are all about the process, understanding the process. The better you understand the process, the stronger your human-machine interaction is going to be. And to do that, we have to solve this process-gap issue. You see, digital worker analytics, think of this as emanating from understanding the human inputs and outputs relative to a machine that might be in recording that occurs in looking at what a human is doing on a machine that might be brought into an analytic environment where you’re coalescing and summarizing human patterns across 50 or 100 different workstations and looking for anomalies, looking for areas that can be automated. That’s an important piece of this.

You then have traditional process mining, which in Fortress has a different way of doing this, but traditional process mining, you’re going in to log data—you’re analyzing that to get a view of that end-to-end process, looking at it from empirical data coming from the running process, building a set of technology.

Of course you have customer journey analytics. This is looking at what the customer is doing as they move on their journey from the more persuasive-type of interactions at the front end of the journey to the more service-type of interaction once they’re a customer. Understanding what they’re trying to do, their context is another whole area of study and area of gathering channel information to understand that. You have, on the left, process analytics had come from a process orchestration that’s built. 

The issue in the industry and I think going forward is that we have these software capabilities. They’re platforms that tend to focus on one or maybe two of these four areas, but they’re not bringing it all together. What we really need for the future of process is to get metadata from all these environments, bring it into a data environment that can then be analyzed and understood with machine learning and other techniques to be able to drive the running process. We’re not there yet, but we’ll get there. So with that, I’m going to turn it back over and we’ll have a short discussion. 

Jon: Thank you, Craig. Thanks for the update and thanks for sharing that autonomous enterprise model which is not yet published. That’s great and it’s always helpful to get the analyst’s opinion. Because you guys are exposed to many different companies and industries, you tend to see patterns and trends before anyone else does in a lot of cases. I certainly appreciate that comment on the digital muscle. Like a lot of people in the COVID era, I haven’t missed too many meals these days. I’m going to tell my doctor next week that I just have digital muscle. I got to get used to the new normal. Anyway, I’ll let you catch your breath for a second and get Stephen into the conversation here.

So, Stephen, we’ve heard from Microsoft that you guys saw two years’ worth of digital transformation in two months in those early stages of the pandemic. Just curious, at that pace continued, can you provide the sunny stories from the front lines? Who’s doing good? Maybe not doing so good? Feel free to just give us your color on the situation these days. 

Stephen: Yeah, absolutely. I would say the good thing is, things have slowed down a little bit. Suddenly, we’re seeing customers settle into this new mode of operation, and that’s particularly important because at the beginning it was this mentality that maybe the pandemic was a little bit like a sprint, when in reality it’s much more like a marathon. People are going to be operating in this mode for quite a long period of time and it’s causing permanent changes to happen across industries and across organizations. 

In the first two months, there was definitely a huge surge in getting systems that were previously hadn’t been touched in years or decades to a place where they could interact digitally with the other pieces of organizations. Luckily, things are settling into a more sustainable way of operating right now, and that is what we’ve seen. Now, that doesn’t mean, though, that there isn’t this huge backlog and demand for digital transformation that’s being accelerated, but I don’t think the acceleration is accelerating anymore. I don’t know what you call that in physics terms—the velocity cubed.

But to the point that you raised as well of, are there organizations that have done it well, and the organizations that haven’t done it well—we’ve seen even from both internal to Microsoft, and publicly, that there is a very wide spectrum of some companies and organizations. Even as Microsoft, in the first couple of months, we were working with educational institutions, we were working with public health institutions who had to be able to rapidly respond to things like COVID testing. 

In fact today with the power platform, we have many examples where COVID testing is still happening, it’s still out there, and it’s being built at running both from local all the way up to national levels on the power platform in an automated way. That was something that just wouldn’t be sustainable to build from scratch using the tools of the yesteryear where you had to build writing code, all of those things from day zero. That’s an example of where things have gone well because they had to.

On the flip side, there definitely are places where government agencies otherwise have not done quite as well. Generally, that’s happened where there have been these legacy systems in place, and there hasn’t been the appetite to put an automation layer between what people are facing, and the back ends. 

There is this story some people might be familiar with, where for certain unemployment systems, they actually put out an article saying please stop automating this unemployment system because we can’t handle it in the back end. That’s definitely an example where if you’re not building the right pieces in between what the users are interacting with and the back end, you’re going to run into problems. We’re seeing less and less of that now, that people are getting used to this “new normal,” but it’s definitely something to be cognizant of across all different types of organizations. 

Jon: I like that concept of transitioning from the sprint to a marathon, and I was actually thinking of the same example, it was with the paycheck protection processing. Essentially, the technology wasn’t too good and was creating too much of a backlog, and they told people they had to stop using RPA to process all those documents and contracts that were coming through. 

Craig gave us a good look into this potentially post-pandemic workforce over the next 10 years or so. What’s Microsoft’s view on changes to the workforce over the next decade, and the impact that technology will have? Earlier this month, Microsoft’s now not planning to reopen offices until July of next year, and 50% of work can be done remotely moving forward. What do you see coming for the workforce of the future, and especially in that 5-10 year range, beyond the initial crisis response, but not out in the science fiction land?

Stephen: It is interesting. For Microsoft ourselves, one thing to call out is we actually are seeing different velocities in different parts of the world. For the United States, for example, that was that July date, but there are other parts of the world that are doing much better. They’re already back in the office. What we’ll see are definitely different speeds that organizations are going back to working in offices, we’ll also see different proportions, and this is exactly what Craig was talking about. As you go down that list, there are some industries where you’re going to get to 8% remote work. But in other industries, you’re going to get 35%. 

What is particularly important is as people are working remotely, how do they have the tools to be successful in that environment. That’s something that will require in some cases up-leveling the digital literacy of the workforce to a point where they can be more familiar with things.

At Microsoft, we often talk about Microsoft Teams, but we all know that’s not the only video conferencing software out there—today, we’re using GoToMeeting. But up-leveling the digital literacy of the workforce is going to be really essential. If half the people are working from home, they have to be able to accomplish, not just get by but to really thrive in that digital environment, or else we’re going to lose a massive amount of productivity from the world economy. Finding ways that organizations can thrive digitally and that the workers can thrive digitally is going to be really essential. 

At Microsoft, one of the things we talk about is this idea of what we call the Remote Work Platform, which is built on top of Microsoft Teams and leverages the power platform to be able to give people ways to build out automation themselves, to be able to give people ways to use the chat interfaces inside of Teams to be more productive with their organization. The workforce overtime will transform from just being a consumer of platforms like Teams, of GoToMeeting, or whatever it may be, to also bring their knowledge of the processes that they have to transform them to be more efficient because you have to be more efficient with everybody working remotely.

Jon: That’s great, and obviously, this Work Remote opens up a whole nother issue around compliance, and how do you understand exactly what’s going on with your workforce that’s now spread out all over the world, but that’s probably a topic for another day as well.

Turning back to Craig a little bit, looking at that human-agent teaming, and the autonomous enterprise model, again, you walked us through those four patterns of interaction—the human in control, the machine in control, those at each end of the spectrum seem pretty straightforward. It’s that semi-autonomous layer where humans and machines share authority in that gray area which is a bit more curious, can you provide a bit more color, any examples of it in practice? My mind keeps going back to Matthew Broderick in WarGames, and the struggle between AI for human-AI versus human, and who’s going to be in control? That reference is probably too young for Stephen, I would guess, but some others in the audience might have picked it up.

Craig: Unfortunately not for me. You’ve picked on the one that is in some sense, the most interesting, and also maybe the most poorly defined at this point. But here’s an example, one of the areas that intelligent automation is jumping on is document-centric text analytics. We call it intelligent document extraction—basically going into forms, emails, and documents using natural language processing and maybe some surface automation to extract clean sets of data. 

The machine will do that, and maybe present confidence levels for fields that are extracted. If the confidence level, in terms of its accuracy, is below a certain threshold, it routes the situation to a human, and the human makes the final call on the accuracy of that extraction. That’s an example where it’s semi-autonomous, the machine is doing things that the human doesn’t understand how it’s being done, but the human is in the loop to share that authority.

The machines are doing a lot of decision management in that it’s saying, these 27 fields that I’ve extracted are perfect, and I’m not even going to show those to a human because I’m smarter than that human. But these three, I need to just send off to have another look at. That’s one where humans and machines are sharing authority, it’s not a closed loop. The machine is not taking that clean set of data and then determining the sentiment based on that coming from a customer, and then handing it off to somebody to deal with that sentiment, it’s bringing a human into sharing that authority. 

That’s an example that I think is good, but that pattern three is the one that has the most opportunity for humans and machines to do exciting things together. It’s your intelligence augmentation aspect there, a lot of the level two patterns that the RPA market’s so enthralled with are relatively simple task automation that you want a human to direct. But they’re not doing anything a whole lot different than what was being done before. They’re just listing out the human inefficiency. But level three and four, but for three gives you a really interesting way to think about the process in a very different way. 

Jon: Building on that a bit, Stephen, AI has been described as “infinite interns,” and having this immense but really non-expert capacity. When I first got involved in AI, 7-8 years ago, we used to say, if I can teach an intern to do it, I can teach the computer to do it, but if I require a PhD to do the work, I can’t teach the computer to do it. Given the advancements in the past 5, 6, 7 years, that’s starting to be a bit shortsighted. What’s your view on whether you can really combine the power of humans and machines through intelligent automation? 

Stephen: The example that Craig just laid out is actually perfect, and we can do that today with the technology that we have inside of Microsoft. One of the things that’s really important about enabling the infinite interns to do something is that you have an experience that can be used to train that AI, but that doesn’t require a whole lot of expertise in how the AI works in the back end, and understanding how machine learning works. Those types of things are very challenging concepts. If the person who’s trying to set up that process has to understand the intricacies of natural language processing, they’re never going to be successful in developing the models that are required to actually execute the process.

One of the things that we’ve been very passionate about is not only do we have the AI chops under the covers, but also surfacing AI through experiences that can be very easy to build, and get started with. Microsoft has talked about this idea of responsible AI principles. One of those key principles is transparency. That way you understand what the AI is doing, at least in terms of the output, even if you don’t understand the inner workings of every hyperparameter that goes into that particular AI calculation.

What it comes down to is how well can we build experiences to train the AI. That way, the humans who are actually part of that process can trust it because they understand what it’s outputting. As Craig mentioned, there’s some level of confidence that it’s always returning, so as long as we’re transparent with that, then you can have really any type of process that’s built out being used by people building out the process, leveraging that pool of “interns.”

Jon: That’s great. Obviously, that’s an issue of AI ethics and transparency is massive, and again probably a great topic for another session as well. How can we overcome this anxiety, given it will be difficult to re-skill workers in the short term. Stephen, you want to take that?

Stephen: Yeah. One of the things that we’ve been focused on is how do we lower the barrier to entry for automation. That way the people who are today doing those tasks can build out the automation and up-level their own skills themselves. This dovetails with the question that we were talking about a while ago is, how is the workforce going to change? One of the ways that the workforce is going to change is the people who are simply just executing that business process today, will be able to infuse automation as a part of it. As we think even about this pyramid that we have on the screen, it doesn’t remove humans from the loop entirely until you get all the way to the tippy top of that pyramid in the fourth layer of the pyramid. People are still a core part of the process.

From an anxiety point of view, as long as there’s still some need for that human intelligence, the decision-making capabilities that we have over machines—I don’t trust those infinite interns to make the core decisions of our business—you are going to be able to still have those combinations of humans and the automation that humans build working together. That will alleviate some of the anxiety that folks are facing.

Jon: Craig, you talked a bit about the process gap and desire of companies to address it. To me, this suggests this lack of detailed operational insights is a missed opportunity for companies, and if used effectively, could create a competitive advantage. What do you see as some of the top areas across the enterprise that could benefit?

Craig: I think the whole thing here that gives you optimism about getting a better handle on the operational processes is the fact that you can transition from the long consultant lead—and I love consultants—but the length of time and the length of the project when you have your traditional document thesis with human consultants doing interviews, and surveys, and so forth, and moving that to grabbing data transparently from these processes. If you see the digital worker analytics on the process binding, that’s what they’re doing. They’re actually gathering data and empirical data from the running process and applying advanced analytics to that.

That’s really the key here to get a more efficient insight, to get better insight, and get it more efficiently. You look at the ability to access data from 1000 workstation interactions across 100 humans and be able to synthesize that, and machine learning is brilliant at pattern, manipulation, and understanding, it’s perfect. You compare that to having to go out and try to understand what those people are doing. It’s just that technology allows you so much opportunity to get a better understanding of the operational process and ultimately to direct that process in real-time through orchestration and through automation. The ability to light up an algorithm that’s in the cloud to make a decision or light up a task automation digital worker to go do something based on what’s going on, that’s the real value of where I see this going.

A lot of companies are starting with the business shared service areas because there’s a lot of inefficiency in those areas. If you look at finance and accounting, if you look at HR, if you look at sourcing and supply chain and procurement, and these areas that every company has, those are the older systems in a company. They’re not the ones generating revenue, they’re not the ones that have been part of big transformations, there’s a lot of inefficiencies there. A lot of the acceleration of intelligent automation has been focused on clean extraction of low-value tasks and labor out of those areas. All of this lends itself towards the exploration of those processes. But that’s only the beginning because the line of business, the real processes that run companies ultimately can benefit tremendously from all this technology. I’ll stop there.

Jon: Cool and I’ll let Stephen wrap things up for us here. Once that process-gap has been addressed, it becomes then an issue of process orchestration, and managing, and executing those activities based on the data. What can we expect in this area in the next few years?

Stephen: You’re going to see a lot of organizations moving into this space. Even in just the past few months, there’s been a lot of focus on process automation from some of the bigger players in the market as well—Amazon has Honeycode, Google released their application platform, Celonis bought Integromat. There’s going to be a lot of options available to companies once they understand what the gap is, how they automate that, and how they build the processes on top.

Obviously, I’m a little biased because I work for Microsoft. We have a really great tool that folks can take advantage of, but you’re going to see a lot of different options and a lot of activity in the space. In the end, the competition would be great for all of us because it’s going to be able to expose all of the different ways that folks can be more productive, that they can up-level their skills and their capabilities in these very challenging times that all of us are facing. I’m excited about the upcoming capabilities, and all the things that will be built in the market in the next few years.

Jon: That’s great, and that’s a great point to end on.

To recap today’s episode of hello, Human with guest Craig Le Clair from Forrester Research and Stephen Siciliano from the Power Automate team at Microsoft. We were fortunate to get the latest thinking from two real industry leaders. With the ongoing return to business, effectively implementing intelligent automation at scale is crucial for success in the enterprise. However, this remains a challenge with large organizations because of a lack of resources to streamline business process discovery, gather accurate data, and then easily kick off automated workflows. By combining the strength of FortressIQ’s process intelligence and Microsoft’s Power Automate, organizations can quickly detail their current state operations, pinpoint the most optimal areas for automation, and execute RPA and automation initiatives on the fly with minimal coding required.

That’s a wrap on today’s show. Thank you, Craig and Stephen, for joining me, and for FortressIQ’s sponsorship. I’m Jon Knisley, and this has been hello, Human.

Episode 4 – Building a Resilient Digital Core

Episode 4 – Building a Resilient Digital Core

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In this episode, we explore the building blocks to developing a resilient digital core, and how automation helps businesses manage through a crisis. Today’s guests are Abhinav Kolhe, Head of Technology for Intelligent Automation at Cognizant, and Sudhakar Pemmaraju, North American Head for the Digital Strategy & Operations Transformation Consulting Practice at Cognizant. Maintaining continuity and managing through change is an existential challenge, as business processes experience enormous demand spikes. By using process mining to more effectively orchestrate process automation, companies can address both near- and longer-term operational challenges.

Cognizant (Nasdaq-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating and technology models for the digital era. The company’s unique industry-based, consultative approach helps many of the best-known organizations in every industry and geography envision, build and run more innovative and efficient businesses. Cognizant believes that the opportunity presented by technology has never been greater, and because of that opportunity, the company will continue to collaborate with clients to modernize their businesses, making everyday life ever better for them, their customers and the communities they serve.

Talking Points:

  • Hyperautomation’s key characteristics and components
  • Building resiliency through hyperautomation
  • Current environment and use cases
  • Building blocks for a resilient digital core


Full Episode Transcript:

Hi and welcome to hello, Human, a podcast to explore ideas and feature humans working in AI and technology.

Jon: Abhinav Kolhe and Sudhakar Pemmaraju from Cognizant join us today on the hello, Human Podcast where we discuss the latest topics in artificial intelligence and how it’s being applied in the real world. I’m Jon Knisley, the host of hello, Human and a long-time technologist.

A big thanks to FortressIQ for sponsoring the program, and be sure to subscribe wherever you listen to podcasts. On this episode, we’re going to explore the building blocks to developing a resilient digital core, and how automation helps businesses manage through crises. Welcome, Abhinav, and Sudhakar to the program.

First off, Cognizant is one of the world’s leading professional services companies. With more than 200,000 employees globally, you benefit from seeing how a lot of different companies in different regions and different industries have responded to the challenges created by the pandemic.

Maybe you can start by giving us a background on your roles and how the automation and transformation market has shifted due to the current environment. I hate to call it the new normal, I’ve done my best to avoid calling it that, but all indicators suggest that this is the way it’s going to be for a while. Sudhakar, do you want to start?

Sudhakar: Hello, Jon. Glad to be on this podcast, Jon. I lead the North American Digital Operations and IPA Advisory Practice at Cognizant. I’ve been with Cognizant for the last four years, and I’ve been in the advisory consulting practice of strategy and operations all my life.

Jon: Great, Sudhakar. Abhinav, you’re a bit more focused on automation in your work, so I’m interested to hear how your perspective on the impact of the current crisis and how it is shifting, thinking on automation among your clients.

Abhinav: Hey, Jon. Thanks for having me. I have been part of Cognizant for 20 plus years now and lead the technology office for Intelligent Automation practice at Cognizant. As part of my current role, I am responsible for practice development on advanced technologies like AIML, BPM, analytics, mining, and discovery tools.

To answer your question straight, I lately first acknowledged that new automation technology can address all the business impacts of COVID-19. We have seen in the recent past the way things have changed. It feels as if we have all done five years forward in this pandemic from a digital adoption standpoint.

The transition looks so very visible to all of us. If you look at banks, we see how smoothly they have transitioned to the remote sales and services teams, and how they have launched digital channels to engage their customers for making flexible payments of debt loans and mortgages.

Another popular example is our schools. Our kids go there. Schools have devoted 200% to online learning and digital classrooms. Doctors are another example in our common life. They began delivering telemedicine aided by more flexible regulations. These are just a few examples of how companies and businesses are working in the current crisis.

Two or three important things I would like to highlight as to how they would further pivot their journey. First of all, companies will need to ensure that their digital channels are on par to succeed in the current environment. Secondly, I think as the economy goes back in the next few months (I would say probably) demand recovery will be unpredictable. There would be the uneven spread of recovery across geographies, across products, customer segments, and whatnot.

Essentially, this will complicate matters for leaders as they grapple the way to deal with an uneven recovery that they are going to see. Historical data and forecasting models that they may have built in the past may not actually work because they don’t know how uncertain the recovery could be.

New data models have to be built. New analytical engines have to be built which will make life easier for operations. Essentially, to answer your question, automation-first or digital-first mindset will be an absolute essential center stage that companies will have to prepare for. We are seeing an upsurge from a lot of customers in terms of Hyper Automation use cases. Questions around what new platforms and toolkits should we actually bring to the table to deliver Hyper Automation use cases.

Jon: With all the buzz around Hyper Automation, I believe it was Gartner that labeled it the number one strategic technology trend for 2020. As a global service provider, how do you characterize Hyper Automation, and more importantly, what are the different components that a leading enterprise should be utilizing?

Abhinav: Yes, absolutely. In a very simple term, I would say Hyper Automation is end-to-end automation, accomplished by harnessing the power of multiple technologies from RPA, to machine learning, to AI, to process mining, and discovery tools, which essentially enables automation for virtually any repetitive task, as well as cognitive tasks for business users.

Hyper Automation goes beyond deploying bots for individual tasks. It is no longer just task-based automation. We are talking about a connected enterprise-wide change program that connects multiple teams, multiple work streams across an enterprise. The Hyper Automation platform consists of many tools. Some of the tools could be BPM platforms like BPM Low- and No-code platforms, like power automate as an example, or Unqork, Mendix type platforms. Process mining discovery tools like FortressIQ solenoids, AIML tools, and platforms. All of these toolkits integrated and stitched together in a common fabric. That’s what the Hyper Automation platform would essentially look like. 

One of the most key attributes of that platform would be the ability to loop in humans in the process. We call it human in the loop but typically, that’s one of the most important attributes of the Hyper Automation platform as well.

Jon: Tying this concept of Hyper Automation to resiliency, Sudhakar. I saw a report in Harvard Business Review a few months back that noted companies that had a better understanding of their processes, that had taken time to map their workflows, were less impacted by supply chain disruptions during the early stages of the global shutdown. They knew exactly which suppliers, sites, parts, and products were at risk which allowed them to put themselves first in line to secure constrained inventory and capacity on alternate sites. 

I’m curious if you’ve seen other business areas where Hyper Automation has supported resiliency.

Abhinav: Absolutely. I can give a couple of examples. Recently, a leading US alien company used virtual agents, human agents to quickly implement a way to process all refund requests caused by flight cancellations at the onset of the pandemic. Speeding the resolution, saving time, lowering cost, and more importantly, improving customer experience.

An insurer applied the NLP techniques along with the smart data ingestion tool, using scanning technologies to analyze policy terms to assess the impact of COVID. This allowed them to capture the data, extract it, interpret the information from documents, deal with cancellation requests, change the coverages, and address increasing transaction volumes.

Obviously, this has made the customers happy and improved customer experience. One more example is a large bank in India used NLP and AAP solutions to implement a government-mandated loan amortization program. This gave the bank’s customers the option to delay the loan and credit payments.

These solutions were actually used to rewrite loan agreement documents and communicate with customers. Doing so, the bank was actually able to reduce call volume significantly by diverting the most frequent request to cell service channels, to online FAQs and forms.

Now coming back to the supply chain. Predicting supply chain disruptions, so we have seen now this two-step process has a measurable operational impact during the pandemic. One leading US-based pharmaceutical company, we worked with used process mining, and machine learning to predict supply disruptions, automating alerts for inventory management and monitoring the supply chain. This actually helped this pharmaceutical company to identify vendors with potentially restricted shipments. It also identified countries with higher risk, and then implemented automatic alerts for open purchase orders based on the risk.

It also allowed these pharmaceutical companies’ purchasing function to continuously predict cycle times, and flag delivery time risks, helping it with production planning.

Jon: To take advantage of those use cases that you discussed, a company needs to implement some programs or initiatives, and I’m a big fan of frameworks because they can really jumpstart a project and often provide a proven path to success.

In terms of a framework for building a resilient digital core, I saw Cognizant promoted recently six building blocks to creating a resilient digital core. Could you recap that approach for us and provide some color on the best practices?

Sudhakar: Absolutely, Jon. Me too, I’m also a big fan of frameworks, because Cognizant, all our frameworks are mostly driven by execution led experiences. The one we are talking about here is the six building blocks for Resilient Digital Core. There are six. I’ll probably cover five of those and let me now take the sixth one.

The very first one is to establish a service demand catalog to understand the end-to-end value chain of the processes across all the departments, with a backlog of potential interventions to address bottlenecks through automation.

Including process mining, process simplification, process standardization, and business improvement techniques. That’s the first one, establishing a service demand catalog. The second one is very critical is managing outcomes. There should be a clear understanding of what is success, defined process metrics, such as criteria linked to all the digital initiatives downstream. This I believe some of the companies are not paying significant attention. It’s very critical to make sure the outcomes are managed. 

The third component of this framework is gaining sponsorship from key stakeholders. In this case, we are talking about sponsorship from each layer of management to drive the digital agenda in terms of training of resources, executing projects, and also realizing the outcomes of the benefits that have been established.

As we all know that when we are going through this digital disruption and trying to be resilient, change management is very critical. The fourth one, I would say manage change and develop a very clear communication strategy. Developing these protocols to articulate goals, educate resources, will actually help create enthusiasm across the business on all the stakeholders by showcasing success stories, describing the benefits, the power of their involvement, acknowledging associates, and developing the skill sets.

The next one is about a talent upskill. Upskilling and rescaling the resources is critical. Many IPA tools demand little to no-coding. However, that said, companies need focus teams that include data models, process mining specialists, architects, and developers. A prevailing trend is to improve the skills of process associates to handle simple automation deployment, and these are the five elements of the framework I covered and I’ll let Abhinav touch on the platform and technology.

Abhinav: Yeah. Sure, Sudhakar. Choosing the right platform and technology for building a resilient core is absolutely essential, I would say. Obviously, many companies do it the way they think is right, but what we have seen for experiences is that appointing a steering committee to represent operating businesses, the IT, the CXO stakeholders, and how will you bring an organizer strong vendor ecosystem to create that technology capability for smart orchestration is what it is all about, but how do you choose a platform or a technology?

What I am seeing in the real world right now is that choosing the right Hyper Automation toolkit is basically being followed in such a way that people or customers tend to follow RPA vendors’ toolkit. Many times that is what is happening. Through our experiences, what we believe the right approach should be to first look inside.

What is the entire gap of tools that exist already in your enterprise versus what is available in the market? Do the data gap analysis, identify the differences, and talk to your IT stakeholders. I can use some examples. For example, every organization would have a BPM platform in place. Every organization would have some kind of OCR toolkit that they may have already implemented in their previous life. They may have an analytics or AIML platform to enable through your Data Science Center of Excellence. 

The point I’m trying to make is that don’t just blindly follow an RPA. Just because you have an RPA tool and that vendor has got Hyper Automation platform, it is not necessary that you just choose that platform to be your Hyper Automation platform. It could be well a choice that you make based on what you already have, do a data gap and then figure out how you can integrate and stitch all of these things together. That I think is one of the key learnings from our experience on the ground.

Jon: That’s very helpful and the approach makes a lot of sense. We’ll make sure to provide a link to it in the show notes to the article for anyone interested in exploring the model in more detail.

In terms of rescaling, just one comment, too often we hear that the fear of automation, it’s going to be this massive job killer and major disrupter. I try to advocate a more utopian view of technology, and my hope and belief are really that Hyper Automation and the related technologies really ultimately aid the worker by allowing them to focus on higher-value work and eliminate the more tedious tasks from their daily work activities. That’s probably a topic for another episode. 

To recap today’s conversation with Abhinav: Kolhe and Sudhakar Pemmaraju from Cognizant, certainly one of the largest but most importantly, one of the most admired and innovative companies in the professional services market. We got great insight on how process automation helps businesses manage through times of crises and explore the building blocks to developing this Resilient Digital Core to help companies navigate and ultimately thrive in challenging times.

Thank you Abhinav and Sudhakar for joining me today. I want to give you the opportunity to make a closing comment or provide some final thought, but I also have one final question for you. I’m a bit of an information junkie and always looking for the latest and greatest resources.

My final question for you is what resources, website, newsletter, podcast, email blast, framework, anything at all, do you rely on most to be successful and knowledgeable in your role? Abhinav, why don’t you start us off?

Abhinav: To answer your question, absolutely. I think my current work that I do or in terms of practice development, uses the most amount of knowledge and is on top of things when it comes to Hyper Automation and stuff like that. Obviously, many people follow some of the popular research firms like Gartner’s and HFS from the world, typically, but I also do follow Twitter, a person by the name Cassie Kozyrkov. She’s the head of decision intelligence at Google. I typically follow her for her topics in analytics. The way she talks on analytics, you should just listen to her. You will be just amazed by the clarity of how she brings around topics like statistics versus analytics, and how these topics differ, and stuff like that.

That’s what I typically do, apart from my talking to vendor partners, looking at CB insights, looking at the top AI vendors in the market, doing capability understanding of their presentations, and I happen to be doing the same talk with FortressIQ as well. That’s my two cents on that.

Jon: That’s great, Abinhav. How about you, Sudhakar, any final insights and your go-to resource for staying ahead of the curve?

Sudhakar: Obviously, one of my go-to resources is we now on this team. They are on top of the trend, we follow that. In addition to that, I always stay active on social media a bit, LinkedIn, podcast, or TED Talks. Constantly, I try to dedicate some time to go to social media and all sorts of social media. That’s one.

We also regularly talk to our vendors, partners, and clients to understand what’s happening in the landscape. That’s one more source of data for us. In addition to that Cognizant has a central research team that collects data from multiple sources and provides us the data.

Jon: That’s awesome. For reference, my resource this episode that I can’t miss is MIT’s The Download daily email brief, touches on a lot of current topics around AI, just a great daily read to get a quick check on the industry. We’ll be sure to put a link to all the resources recommended in the show notes. That’s a wrap on today’s show. Thank you, gentlemen, for joining me. Thank you FortressIQ for sponsoring. I’m Jon Knisley and this has been hello, Human.

If you enjoyed this episode, subscribe and check out our series at fortressiq.com/podcast. Thanks for joining us today on hello, Human.

Episode 3 – Rebooting Data-to-Decisions Initiatives

Episode 3 – Rebooting Data-to-Decisions Initiatives

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Today’s guest is Doug Henschen, the Vice President and Principal Analyst of Constellation Research. Today Doug will be offering the analyst viewpoint concerning the role of process orchestration in helping companies manage new information, closed-loop analytics, the best integration of technology in both online-based and brick-and-mortar companies, as well as his view of the trends in human-agent teaming.

With the on-going return to business, leading organizations continue to pursue digital transformation and innovation goals even as they seek efficiencies and optimization. These initiatives depend on data, often data at scale, and technology optimization to meet target objectives and achieve the target future state. 

Doug Henschen is Vice President and Principal Analyst focusing on data-driven decision making at Constellation Research Inc.. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. His research also acknowledges that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, business intelligence, data-visualization, analytics, NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Henschen has a Bachelor of Arts, Syracuse University and has been in this field for almost 20 years.

Talking Points:

  • An introduction to the firm and Doug’s research focus
  • The role of process orchestration in helping companies manage new information
  • The challenges of closed-loop analytics 
  • Real-world stories about consulting with different industries
  • Prognosis trends around the issue of human-agent teaming and partnership
  • The resources Doug uses to stay informed in the world of consulting and data analysis 


Constellation Research

Constellation Research Business Transformation 150





Full Episode Transcript:

Jon: Doug Henschen, Vice-President and Principal Analyst of Constellation Research, joins us today on the hello, Human podcast where we discuss the latest topics in artificial intelligence and how it’s being applied in the real world.

I’m Jon Knisley, the host of hello, Human, and a long-time technologist working at the intersection of business and emerging IT applications across both customer experience and operational excellence. A big thanks to FortressIQ for sponsoring the program. Be sure to subscribe wherever you listen to podcasts.

In this episode, we’re going to explore how next-generation platforms are rebooting data-to-decision initiatives for leading organizations. Almost every strategic initiative today relies on data and often data at scale, as well as technology optimization to meet the target objectives and get companies to their desired future state.

Welcome to the program, Doug. I’m excited to hear the analyst viewpoint today. Much of Constellation’s work focuses on how business models can be transformed by disruptive technology. I first heard about the firm a few years back with the publication of the now annual BT150, which is that list of 150 or so global executives who are leading innovative business efforts in their organizations. 

Maybe you can start, Doug, by providing a quick introduction to the firm and your research focus, as well as some comments on the trends that you’re seeing in the current environment—what obviously is not a technology-driven disruption. Certainly, the pandemic has disrupted business and the impact—at this point, it’s pretty clear—will be felt long-term.

Doug: Yes, John. Technology can help you weather this storm and deal with change in other contexts. It’s great to be with you. To tell you a little bit about Constellation Research, we’re a 10-year-old tech analyst firm. The same category as a Gartner or Forrester, but we’re really a boutique specialist tech analyst firm. We totally focus on what pioneers and innovators are doing, how they’re embracing technology to really change the way they do business, dramatically differentiate themselves, move that needle, and differentiate themselves.

Pioneers and innovators are only 5% of companies out there that can be in that leadership category, but we spend all our time with these leaders. We do a lot of case studies. We do a lot of different types of research around this innovation, and then we try to share it with fast followers. These are the companies that don’t want to get left behind, that’s maybe 20%–25% of companies. They aren’t necessarily the leaders. They don’t want to hit the same landmines and roadblocks that these pioneers and innovators overcome. That’s the idea is to share the insights and knowledge from those leaders and innovators.

What we don’t do is rubber-stamp the 20-year-old buying decisions for cautious adopters and laggards. I, myself, have been at Constellation a little over five years. My research domain is called Data-to-Decisions, which actually is very broad. It covers a lot of things—ingest, transform data, integrate the platforms that we see today, the use of data, BI, analytics, data scientists, and data science. But it’s not about any one of those silos. It’s about that end-to-end process of taking data and using it to drive decisions, using it to drive actions. In this pandemic environment, we’ve seen that the companies that can do that have huge advantages.

Our founder, Ray Wang, wrote a book about digital transformation about five years ago. Now, it’s the topic de jure. We often hear that in the last six months, companies have done more digital transformation than they did in the past six years. It’s all about getting more agile, getting more data-driven. It’s put the move into the cloud on steroids. The companies that are ahead of the curve are in a good position.

Jon: I appreciate that focus on emerging technologies as one who’s been involved in a lot of first-in-class technologies through a bunch of different cycles that can be a challenge. But seeing the impact that they can have in an organization—both the established players as well as the emerging ones—is just massive. So, that’s great to hear, and again, welcome to the program. 

Peter Drucker taught us back in the 50s that what gets measured gets improved. I tend to argue that that was really the start of this journey towards building a more data-driven business culture. Two generations later, we’re now awash in data and have a lot of great tools to manage it.

One of the major challenges that we’re seeing is companies trying to make all this data actionable. What do you see as the role of process orchestration in helping companies manage all this new information that they have?

Doug: When I hear that term, process orchestration, I think of connected, end-to-end processes. The big drive has been to create a transformational customer experience. To do that, you need to underpin it with back-office and processes behind the scenes that aren’t necessarily customer-facing but that has everything to do with delivering a transformational customer experience. 

As for that term, process orchestration, back in 2000–2004, I was editor of a magazine. I started as a tech journalist 25 years ago. Business process management was the thing we talked about. I think the terms have evolved. Today, Constellation sees a convergence ahead. We’re going to see integration, microservices, API management, orchestration workflow, robotic process automation, and AI in this mix of what we’re thinking of as intelligent orchestration that’s maturing. 

It’s not a clearly defined category today, but what is clear is that companies can’t paper over gaps in processes with email, spreadsheets, phone calls, and even paper-based approaches that some companies still use. We’re going to need a combination of humans and machines. But across those processes, you need measurability to drive continuous improvement. That’s really what’s important.

Jon: With this intelligent orchestration, ultimately, do you see technology stacks getting bigger? Is there going to be consolidation or some companies in the same way as we saw with marketing technology? Is it going to be a battle between best-of-breed versus all-in-one solution?

Doug: I think we’re going to see platforms on which people will start to build very custom applications. Instead of these standardized enterprise applications, they’re going to build more and more. There’s just tons and tons of last-mile applications that companies need to build that haven’t been there. There’s just too unique to the organization to have a commercial software vendor provide that application. Companies are going to be using this mix of technologies to build their own applications and experiences on top of what I’d call intelligent orchestration platforms.

Jon: Cool. One topic that I keep seeing and hearing more about is this idea of closed-loop analytics. How do those shift aid companies? What do you see as some of the major challenges in achieving it?

Doug: When I think closed loop, I think of learning. I think of cognitive systems. Let’s put that in context. People have been talking about AI. I think a real shift in the market in recent years was 2011. It’s almost 10 years ago now that Watson beat the human champions of jeopardy. That was a watershed moment when AI came back into fashion. Back then, the talk was of curing cancer, let alone driving better decisions. But it was turning out to be much harder than we thought, much more challenging than the hype was envisioning 9–10 years ago. It’s hard for a couple of reasons. We find that you have to close the loop at multiple levels.

It’s not just closing the loop on data. It’s closing the loop on the metadata. What’s the context behind that data? It’s closing the loop on the graph of human and machine interactions with systems. It has been this long-standing separation of analytics, whether it’s BI or more advanced data science, separation of that from the transactional environment and the processes.

We’ve had this swivel chair integration. Where you’re in one system, and then you go off and you look at the report, or you go off and look at a dashboard to help make your decision. Of course, that’s open to inconsistent human interpretation. That is tied to this second challenge of having the context. It’s not just about the data. You really have to know something about it.

It’s the metadata around that data. Who is this customer? What is their history? What is the stage of the process they’re in? When did we talk to them last? Do they have a service issue that’s unresolved? Are they near the end of their contract? It’s not just data, it’s the context around the data. 

That’s one reason I’ve been doing a lot of research and been advocating embedded approaches that next-generation applications that bring the data and the context right into the context of the application. You have both a human understanding and machine understanding, and you can drive better actions, whether those actions are carried out and executed by a machine or by a human.

Jon: Much like consulting as an analyst, you see lots of different companies across lots of different industries. Can you share any real-world stories with us? Examples of high performing companies that are more mature in their data to insights journey, and really starting to drive value from leveraging their data platforms?

Doug: I’ll use an example that’s often cited by our founder, Ray Wang. He cited it in his book. If you start talking about the hottest stocks of the last 10 years, 2010–2020, everybody would probably immediately think of the FANG companies, Facebook, Amazon, Netflix, Google, et cetera; these born in the internet age companies. You’d be right. If you look at Motley Fool, number one over that last decade was Netflix, but number two is a brick-and-mortar company you might not think of, and that’s Domino’s Pizza. 

If you really look closely at what they’ve done, they’re absolutely a pioneer and an innovator. If you go through their website or their phone app, a customer can track the order. They can see the status of the pizza that they ordered. Who’s making it, when it’s out of the oven, when it’s in the delivery vehicle, who’s dropping it off, and when it will arrive. 

That is process awareness and Domino’s obviously uses this internally for optimization and improvement. They use it for a great customer experience. I’m talking about the delivery and the visibility standpoint. I’m not necessarily a fan of their pizza but they continue to work on this, continue to work on making it easier and easier to interact with the company and to get dinner. 

They did it with a website. They did it with an app. They added a smartwatch. They added a smart speaker. They do everything they can and they keep working at it. It’s totally data-driven. They’re process-aware. They’re continuously improving, and in this pandemic environment, they’ve only been growing.

Jon: I truly appreciate the Domino’s reference. I’m a big fan of the app. My daughter might fight you over the quality of the pizza question. She’s a major fan of it.

Doug: I’m in the New York area. I’m used to the New York basis. My frame of reference is a little different than most people. 

Jon: But I totally get it. They see themselves as a technology company that does pizza, along the same lines, as you often hear Capital One. They’re a technology company that does banking. Great example there.

Doug: I’ll mention another example. I’ll count them. I’m not a stock owner. I don’t have to disclaim this but my own experience. In the pandemic, I wanted to take advantage of these historically low-interest rates. I called my well-known large bank that holds a great majority of mortgages these days, figuring they know me. I’m their customer already. They should be able to provide better service. I was on hold for an hour. I’ve used the bot and left messages. I didn’t hear back for a week. I interacted with Rocket Mortgage, had a lock-in on the same day, I had a great seamless experience. Four or five weeks later, their agent was in our driveway with a mask and an iPad. They wiped it off and almost every document we needed to sign.

I just took that iPad back into our home while they stayed in the driveway. My wife and I could digitally sign almost every document. The only documents that still required wet signatures were those that were required by the state. It was a tremendous customer experience. I’ve since had very proactive communications. This is when your first mortgage payment is going to be due, what portion went to tax, what portion went to the principal, et cetera. Great customer experience. Again, I’m not a stockholder or anything, just another example of a company that has a great digital experience.

Jon: While we’re on that topic, that reference made me think about the other one, in that space a little bit, at least on the financial space, the Lemonade Insurance. The ability for them to process a claim for a minor loss in literally seconds—I think it’s down to 10 seconds now—and money appearing in people’s accounts the next day. Just incredible stories you hear about ease of use and an entirely frictionless experience. Obviously has an impact on some of those larger, more established organizations.

Doug: Yeah. The insurance industry has also raised the bar on having your insurance policy be based on usage. The snapshot type thing doesn’t mess with my discount. Now, every company seems to have a way to capture your actual driving data and charge you your premium based on your actual driving.

Jon: Very cool. Turning to this, you mentioned it briefly before this convergence of humans and technology. We hear a lot about the front-office applications, your work on the more data analytics side, more operational back-office areas, specifically analytics. What’s your prognosis trends around this issue of human-agent teaming and partnership?

Doug: Well, companies in the C-suite are increasingly going to have four choices. When do we trust intelligent machine automation? When do we augment the machine with a human? When is it still primarily a human process but we can assist them with recommendations and suggested actions? When do we leave it in the hands of human judgment, either because of creativity or a lot of intuition, or interpretation is needed where there’s complexity, where there’s risk, et cetera? 

Today, we’ve already seen plenty of examples of bots. Simple bots to more sophisticated digital workers for specific roles and industries. Everything from time-off requests approval, password resets, more complex things where you have voice-text interaction or nuanced interpretation of human intent. More complex workflows like initial screening of customer support calls, not with ABC wrote a menu, but an actual natural language conversation in which the machine can actually discern intent and probably handle the 80% of the calls and then route the other 10% to 20% to the human.

What we’re seeing now is progress is happening. It’s really starting to gel in the uber management of the total team environment, overseeing the toll process with both digital agents and humans. It’s not just a bunch of disconnected silos of automation, these bots. You need kind of an overarching sense of what’s happening in the total process. You need to monitor and load-balance across the humans and the digital agents in the process. You need to be able to measure, optimize, and sense when needs are changing. Then, you can change processes accordingly and retrain both the humans and these digital agents that sometimes you might have to take the agent offline and switch in the human because something is happening dynamically in the business environment. That’s what we see ahead. The progress is now happening, where it’s overall monitoring of the process, not just adding more and more bots to have silos of automation.

Jon: I love that comment on augmentation and the recognition that some processes should and remain human-based processes. I think too often we say, let’s throw more technology at it, it’s all got to be technology, but depending on the experience you’re trying to deliver, sometimes it does require that human interaction, that human contact that people desire. That’s a great point to end on.

To recap today’s conversation with Doug Henschen, the Vice-President and Principal Analyst at Constellation Research, certainly, one of the leading research and advisory firms looking at the impact of disruptive technologies on business models, with the ongoing return to business leading organizations continue to pursue digital transformation and innovation goals even as they seek efficiencies and optimizations. These initiatives really depend on data and often data at scale, as well as technology optimization to meet those target objectives and achieve the target future state. The next-generation platforms that are emerging or are reshaping the data-to-decision journey for many organizations. 

Thank you, Doug, for joining me today. I want to give you an opportunity to make any closing comments or provide any final thoughts you may have. But I also have one final question for you. I’m a bit of an information junkie, always looking for the latest and greatest resources out there. My question to you is as an analyst, what resource—website, newsletter, podcasts, anything at all—do you rely on to be successful and knowledgeable on your role?

Doug: I would caution any technologists not to get too carried away from the need for the technology. My go-to every morning is a thorough read of the business section—The New York Times, Wall Street Journal—something a little deeper than a lot of small-town papers in terms of depth of what’s happening in the business. You need that context and grounding of what’s happening in the economy, what are the business trends, what are the business needs. You can just read the headlines from day-to-day, see what companies are standing out, and how they’re differentiating themselves.

In terms of technology, I would have two sites that I spent a lot of time on, datanami.com is great for news and analysis of all things analytics and data science. Some great reporters there like Alex, Woody, and George Leopold. For a deeper, more scholarly take on data science and what’s happening in data science and analytics, KDnuggets is a great site. Case studies, in-depth how-to, opinion, a rich resource on what’s happening in data science.

Jon: That’s great advice. We’ll make sure to put all those resources in the show notes. For reference, I’ll give you my resource in this episode that I can’t miss and I’m going to stay close to home for you, Doug. That would be DisrupTV, the weekly web series hosted by your colleague that you’ve mentioned Ray Wang and Vala Afshar from Salesforce. There’s always a nugget that I find in every show that I can use for my work.

Doug: Yes. Thanks for the […]. Great weekly resource. 

Jon: It is a great weekly resource indeed. I think that’s a wrap on today’s show. Thank you, Doug, for joining me and FortressIQ for their sponsorship. I’m Jon Knisley, and this has been hello, Human.

Episode 2 – Turning Talent into Competitive Advantage

Episode 2 – Turning Talent into Competitive Advantage


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With me today is Kamal Ahluwalia from Eightfold. Together we are going to explore the current state and future state of turning talent into a competitive advantage for companies. People are really the backbone for any organization, and it’s not an accident when we talk about the golden triangle of people, process, and technology that people are first.

Employment is the backbone of our society, and everyone deserves the right job. Today, you get the job based on who you might know and not what you are capable of doing. Tomorrow, AI-enabled technology will transform how high-performing companies acquire and manage talent.

Talking Points:

  • Who is Kamal Ahluwalia and what does Eightfold do?
  • How can AI technology help veterans? 
  • Can AI technology help with unemployment?
  • Social justice, diversity and inclusion and how AIs can remove bias
  • AI and remote work and school
  • Transformation fatigue 


Website: FortressIQ

Website: Eightfold.AI

Apple News

Twitter: @kahluzalia

Full Episode Transcript:

Jon: Kamal Ahluwalia, the President at Eightfold joins us today on the hello, Human podcast, where we discuss the latest topics in artificial intelligence and how it’s being applied in the real world. I’m Jon Knisley the host of hello, Human and a longtime technologist working at the intersection of business and emerging IT applications. 

A big thanks to FortressIQ for sponsoring the program, and be sure to subscribe wherever you listen to podcasts.

In this episode, we’re going to explore the current state and future state of turning talent into a competitive advantage for companies. People are really the backbone for any organization, and it’s not an accident when we talk about the golden triangle of people, process, and technology that people are first.

Welcome, Kamal, to the program. As you may be able to tell, I’m excited about this conversation, so let’s jump right in. First off, congratulations to Eightfold on the recent selection as the grand prize recipient of the Veterans’ Employment Challenge. That’s quite an honor and validation of the platform.

Although veterans are some of our most dedicated and talented individuals, that transition to civilian employment is known to be challenging. Can you tell us a bit about the Eightfold platform for folks that may not be aware, and how it will help the military community specifically?

Kamal: Great. Thank you, Jon, and thank you for having us and making time for us. Thanks for the congratulations, we’re actually very proud of being able to help. Especially, in my opinion, I think the best-trained workforce in the world. If you actually double-click on the role that veterans play, it’s not just while they’re in the military service. Today, they do represent at least 6% of our workforce.

There are about 20 million veterans in the US right now. I think we haven’t done enough to make it easy for them when they transition. The reason why we got involved with this is we have built an AI platform for all talent. Single platform for hiring, retention, internal mobility, diversity, and inclusion, and all of that. The mission is to apply our expertise to provide the right career for everyone in the world.

As we got going three years ago, the thing that we kept looking at was okay, how do we do it at scale? The thing that dawned on us was if we are going to solve this thing quickly, in the next few years. We have to start with the largest employers and the largest talent pools. Veterans are definitely a very large, very capable talent pool.

When we got this opportunity to participate in this Department of Labor, Department of Defense, Veterans Affairs challenge. That brings your best technology to bear in helping our veterans’ transition, it didn’t take us long to jump into it. What we have done is this. We have a capability matrix in our platform, and that is geared to learn from all the people in the world, and all the publicly available data to understand what they are capable of doing, and it all depends on the context.

Resumes and all that stuff are all backward-looking. What people are looking to do, and what we want to use our AI expertise is to allow the hiring companies to hire for potential. With veterans, that is the problem. They are very well-trained. Most of the time they are working with technology that most of us won’t even see for another decade. And then all the soft skills that people ask for—leadership skills, ability to perform under pressure, ability to work in an ambiguous environment in this entire year as ambiguous as it gets.

This is the workforce that is best trained to actually thrive, and yet in April, when COVID hit, 8000 veterans transitioned straight from military service to unemployment. That’s a travesty. Yes, everybody’s suffering, but that’s what we want to do to help. When we go back to the capability matrix, what we have done with our data-driven platform is that we should be learning from all the skills and capabilities that are out there.

Focus more on learning ability, not just what you’ve done in the past, understand the context, and evolve every time so that you are accounting for the latest greatest things. With veterans, in particular, there are two tracks to solve. One is understanding what they have done when they were serving in the military.

Some of it is captured in their military occupation codes. Some of that is in their […], which is essentially their equivalent of the resume, but then there are two things there. One is translating what they know into a civilian role, but then a lot of them have very different aspirations. It’s not like they want to continue doing what they were doing in the military service versus whether it was logistics, transportation, or IT services.

In that case, we are actually looking to provide them the opportunity. When I was in one of the sessions, one of the veterans wanted to pursue a career in performing arts in Florida. A few of them wanted to go back to school and learn new things. A couple of them wanted to become state troopers—one in New York, one in Chicago. Another one wanted to move to a different part of the country. 

All of these combinations we should be able to account for and with the AI we can. In light of this is what we are building with them and why I think we were chosen was a very intuitive and easy way for veterans to find a career of their choice. If they want to pursue a career of which requires them to learn new things, give them a solid career path so they can get there, and essentially make it easy so that this transition should not be filled with anxiety. It should be filled with promise and confidence. That’s essentially what we’re looking to do for veterans.

Jon: That’s great. Congratulations, again, on the recognition. I spent some time at the Defense Department’s Joint AI Center and talent management was definitely an area of interest, so it will be great to watch Eightfold’s contributions in the coming years.

Turning to a topic that’s been top of mind for many this year, I want to explore social justice and Eightfold’s potential to address diversity and inclusion challenges. Each week, there seems to be a new story about someone being rejected a half dozen times from an organization until they disguise their gender or their ethnicity.

Can the platform help remove that bias and provide greater accountability? Do you have any real-world examples that you can share with us?

Kamal: Absolutely, and I’ll share a few. What we were talking about a few minutes ago about how to hire for potential and how to use the capability matrix, that essentially is the only way to solve for diversity and inclusion. Because if we keep looking for people who have already done it before, clearly, we haven’t given enough opportunities to people of diverse backgrounds and people who don’t have the same privileges we have had.

This applies to pretty much every segment. In fact, it applies even more so to women in particular because our whole system is stacked against them. The way to get out of that is one, when people are applying, we have to look at the whole thing holistically. No one is asking us to do them a favor, no one wants that. All they want is a fair shot, and that’s the equal opportunity part. 

Our algorithms don’t take into anything into account, whether it’s sex, background, ethnicity, pedigree, any of those things that factor into the match score when an individual is being compared to a particular job. So that when we are presenting these few people are a great fit for this role, it is only based on what they’re capable of doing. 

Then we can even help the company create better job descriptions that are all geared for what you’re trying to do versus leaning one way or the other. Because we comply with equal opportunity algorithms, all these things are taken into account. Even though a company’s data set may still be skewed towards one side, we are able to course-correct right there. 

Secondly, when you create the job description, you are able to see that what does my talent pool surface? How many people would fit this profile? Who is likely to respond? And how many of these people are of diverse backgrounds? If you’re asking for requirements that skew the talent pool against a particular diversity segment, you can do the course-correction right there. 

I’ll give you a simple example. Of all the data scientists who know R only 30% are women. Of all the data scientists who know Python, only 15% are women. If you write a job description and the hiring manager says I want to hire someone who knows both R and Python. By definition, you have stacked the deck against women. You only have at best 10%, 15% representation. And invariably, it’s 1/9, so highly likely that you’ll hire someone else. One or two years later, we look back at the group and say, hey, how come there are no women here? 

Those are the things it’s all data-driven because we bring a global data set to you so that you know whether such people are out there or not. If they are out there, then we make it easy to find them.

The second part is addressing how do we widen the talent pool because a lot of our customers are taking those steps? One of them actually went back and looked at how many people had applied from Howard University, and it turns out 700.

There were plenty of candidates available who would fit the profile. Then some of our customers have improved the diversity from 18%-33% for women. One of our customers, Micron, has recently been sharing that they are able to identify the ethnic background for 77% of the candidates. That enables them to take affirmative action if they choose to, but at least become more and more diverse. 

Even other customers have seen a 90% reduction in time to discover and engage with underrepresented candidates. In all aspects, the number one excuse that is often used is that we looked, but we couldn’t find enough capable people of diverse backgrounds. We want to use our AI to show that no, you didn’t know how to detect the potential, and we are here to help you.

If your intent is there, we want to make sure that you can find the diversity candidate that you want to bring in and make your workforce more inclusive. The last thing I’ll share on this one is on career sites, when we use our matching algorithm, we also bring transparency.

Here, people don’t have to figure out on their own on which job is a good fit for them, but we are able to tell them that you’re a great fit for these two or three roles, and here’s why. That allows them to apply with confidence, and we are seeing a tremendous uplift of 30%-50% improvement in diversity applicants applying to the right roles where they are likely to be a great fit.

That’s changing the dynamics for our customers who want to solve for diversity, but earlier systems or technologies would simply not be able to do that, and we are absolutely loving the opportunity. Unfortunately, it’s always because of these adverse news items that come up and how individuals are being targeted, et cetera, but we’re hoping that this time people stick with it and actually make a difference. I think we are seeing more and more companies stepping up.

Jon: I think having that information and then transparency enables those companies to stand up and do the right thing, which is great. The other major shift that we’re seeing this year with the workforce is the transition to remote work, which is appearing to be more and more permanent trend that we need to adjust for.

As a leader in that talent management space, what’s your expert opinion on this issue? Is remote work here to stay, or in two, three years are we going to be back in the traditional office environment? Who benefits? Is this an employee benefit, employer? Do both sides of the equation benefit from the transition to remote work?

Kamal: I think COVID, unfortunately, will change a lot of things and accelerate a lot of things because what we are seeing is very clear that all of us are being forced to work remotely. Even though we thought that this is not the way to work, we are all seeing that, yes, you can get stuff done.

The second problem that’s emerging, unfortunately, and we see this and I think we’ve all gotten used to enjoying it. Now kids are also at home. They’re also learning if they’re younger, they need retention. Even if you have both the parents working, that’s hard to juggle both the school and the work.

If you’re a single parent, you have my best support and wishes because it is so difficult. What that’s leading to is, yes, kids will go back to school. They also need their environment, but I think some of these things are requiring us to step back and think very differently. I do see a couple of very clear opportunities here.

One is that we will cast a much bigger fishing net because the talent is there. We’ll all get adjusted. We can hire in cities where we never went in before because we didn’t think they were close enough, or we didn’t have an office there. Well, guess what, we don’t need an office in every city or wherever people are. As long as the talent is available, it will allow us to cast the net a lot wider. 

Secondly, if that leads to 5%, 10%, 20% improvement or increase in your talent pool, then you need AI to go through that very quickly to figure out who are the best people to bring in. You don’t need to do anybody a favor. You simply have the ability to hire from more diverse backgrounds. 

McKinsey did a survey with us using our data on how to help people who did not finish a four-year college on how to get them on a career path so that they are making more money than they’ve done in the past.

The key to that is identifying what enabled them to succeed in the past and then providing them more and more opportunities to get into such roles. Using AI and data to provide better opportunities to others who are not presented. That essentially reflects this in remote work.

Then the third element is how it helps the company? Maybe you are now back to thinking in terms of running multiple shifts or hiring people for specific time zones. As long as people are interested and willing to work at different hours, you can continue hiring them according to that. And that, by the way, has happened with call centers. 

A lot of companies that used to run these in offshore environments, in Eastern Europe, or other parts of the world. The math was that to run a 24/7 call center, you need essentially 5.4 people for every role, and that would give you enough staffing to have a fully manned properly staffed role to account for holidays, vacation, sick leave, and all of that stuff.

Some of that rejiggering of how we think in terms of our workforce is needed. Then the other element is do we have the managers who know how to manage a remote workforce? That oftentimes is not a requirement—some have done it, some have not—but now that becomes a necessary part. If you’re going to be leading a team, you need to know that some of them will be remote, and could be anywhere. 

I think the COVID is actually having us rethink, and I’ll give you another example. Event recruiting or going to campuses to hire for early talent. Guess what, that’s not available, but a lot of the companies are still running their business. 

With our stuff, you can run a virtual career fair. You’re not limited to just going to 30 universities, you can go to all of them. Get all the resume books in, see who you want to talk to, schedule the interviews. And instead of being limited, it’s giving you more opportunities to hire from colleges that you never went to.

I think this is forcing everyone to step back and think about how do I leverage this? Yes, it’s not comfortable, and clearly, we need to get past the health crisis and then the unemployment crisis. I think it’s here to stay. I think things will get better because it’ll force us to accelerate everything.

Jon: Certainly, my son can attest to the challenges of remote school, and hopefully we can get the children back safely into the school soon. I appreciate those comments. Talking with business leaders and a lot of the organizations that I work with, there seems to be transformation fatigue that’s starting to settle in a little bit.

I see more and more eye rolls when the transformation word gets mentioned. Some people say hey, don’t even bring it up. It’s not going to go anywhere. That said HR and talent management, in my mind, still seems very ripe for transformation, especially given the advancements in automation technology.

They can shift human work from low value to higher-value activities, increased productivity, and increase that employee experience. In some ways, I think the pandemic may have increased awareness, but you hear all these dire predictions about robots taking everyone’s job.

I personally don’t buy into that vision and think worker augmentation should be seen as a net positive. What’s your worldview on the impact of AI on the future of work?

Kamal: I love how you phrased it, worker augmentation, because I think what will come up is a better balance between the stuff that can be automated and can be done by someone—basically a machine. Oftentimes, a lot of this menial work or repetitive work is not the fun part of anybody’s job. If some of the stuff can be automated, we should automate it. 

The second thing—I like how you phrased it as transformation fatigue. The thing that’s emerging for us is a lot more focus on people and employees because before this, there was a lot of this thing, hey, we’ll just go out and hire. Now, as more and more people are being forced to rethink their business, and there is more need for digital scales, data scientists, and all of that stuff. People are realizing that it’s not like all these markets are littered with that talent, so let’s focus more on the employees. 

What we have been focused on is how do we get the rescaling and upscaling going? How do we help companies foster and build a culture of learnability so that people are always looking forward to doing stuff?

The thing that a lot of our customers are saying is that look, it’s not just classes because most companies have subscribed to a lot of classes. But most people when they are in a job they learn 70% of their new learning comes from actual experience. About 20% comes from people and mentors, and only 10% comes from taking a class.

What we are using with our AI platform is how do we provide a talent marketplace or a project marketplace where the company can post projects? The important part is how do we get some of the rock stars to participate, and how do we find the experts so that we start building this self-service environment where people want to be appreciated, they want to learn more, or they want to learn from others who are recognized in the companies as being experts?

How do we create that environment, and we are now seeing tremendous success with a lot of our customers were both internal mobility, rescaling, employees wanting to learn new things, and then how to work together as a team. This thing is manifesting itself in a lot of use cases, for example, when you’re doing an M&A.

You’re going to bring two companies together. You want to actually preserve the best talent on both sides, and how to bring them together so that they start working together integrated as a single team. The project marketplace is a great way to do that.

Secondly, we also run hackathons now. Our first one during COVID was actually across three continents was very successful. The fun part was that everybody even got to vote on the projects. By default, they were picking the things that were interesting and things that were not interesting.

There’s a lot of simple ways of actually personalizing the experience. What we are doing a lot is how to change the user experience. One, the individual and employees feeling empowered. That hey, if I want to explore other opportunities within the company, how do I do that?

To me, it is transformational, but I have no issues if we don’t use that word and simply say this is all about empowering the individual to pursue a career of their choice within the company. Focus on learnability, applaud everyone that’s learning new skills and building new strengths, and get it to be aligned with the company direction. When you do that, it is magical. We won’t use the transformation word. Let’s use the word magical because that’s what it is. What do you think?

Jon: I think that’s a great point to end on. To recap today’s conversation with Kamal Ahluwalia the President at Eightfold. Certainly, one of the market leaders in the emerging technology-driven talent management space, really helping companies gain incredible insights into their employees and candidates.

Employment is the backbone of our society and everyone deserves the right job. We heard about the exciting work Eightfold is doing to support our veterans. Today, you get the job based on who you know, and not necessarily what you’re capable of doing.

Tomorrow, thanks to companies like Eightfold, AI-enabled technology will really transform how high performing companies can acquire and manage talent.

Thank you, Kamal, for joining me today. I want to give you an opportunity to make any closing comments or provide any final insights, but I also have one final question for you. I am a bit of an information junkie and always looking for the latest and greatest resources. My question to you is what resource—website, newsletter, podcast, email blast, anything at all—do you rely on most to be successful and knowledgeable in your role?

Kamal: That’s a tough one. Everything else was easy. Apple News is what I rely on every morning. Funnily enough, even Google Alerts. I’m trying to get off social media and somebody else determining what I should be looking at. The other part is, in the last two years, the content that’s interesting to me is changing and is becoming more and more focused on the individual.

How would people learn? It’s very different. It’s more about how to influence, change management because we are seeing a tremendous opportunity to get people to think differently. More than anything, that’s what I’m looking for and I’m interested in learning more and more about it. I think my sources are different now than they used to be.

Jon: That’s great, and I’m a big fan of Apple News myself. I get a lot out of that $9.99 subscription every month. For reference, my resources episode that I can’t miss is the First Look daily email from CIO. I’ll date myself a bit and admit that I used to get the print version of CIO magazine back in the day when we actually had magazines.

They have a great article today on low-code platforms and how companies like Toyota and Conoco are leveraging them to drive business value. We’ll be sure to put a link to all the resources recommended and the article announcing Eightfold’s recent award as well in the show notes. That is a wrap on today’s show. Thank you, Kamal, for joining me and FortressIQ’s sponsorship. I’m Jon Knisley, and this has been hello, Human.

Episode 1 – The Four Realities of Transformation

Episode 1 – The Four Realities of Transformation


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Our guest today is Pankaj Chowdhry the founder and CEO of FortressIQ.

Organizations on the transformation journey should take the counterintuitive approach of embracing both change and the status quo. The key to the speed, and success of a transformation program lies in figuring out how to leverage components of the existing, while delivering change.

This show is supported by FortressIQ, whose mission is to unlock the limitless potential of the global workforce by accelerating the responsible and ethical use of AI in the enterprise.

Subscribe now, wherever you find podcasts so you don’t miss any of the tips, strategies, or tales from the front lines of the submerging and rapidly evolving technology that is impacting how every one of us lives, works, and plays. 

Talking Points:

  • Is there a darker side of AI?
  • Exploring the ideal blend of AI-human integration
  • The Four Realities of Transformation
    • Your processes aren’t going away
    • Your IT systems aren’t going away
    • Your people aren’t going away
    • Your organization’s structure isn’t going away


Website: FortressIQ

arXiv sanity

Farnam Street blog 

Farnam Street podcast

Full Episode Transcript:

Jon: Pankaj Chowdhry, the founder and CEO of FortressIQ, joins us today on the hello, Human podcast, where we discuss the latest topics in artificial intelligence and how it’s being applied in the real world. I’m Jon Knisley, the host of hello, Human, and a long-time technologist working at the intersection of business and emerging IT applications.

A big thanks to FortressIQ for sponsoring the program. Be sure to subscribe wherever you listen to podcasts.

In this episode, we’re going to explore the AI-human integration and the four realities of transformation that lead us to this idea that successful transformation requires a careful and somewhat counterintuitive balance of embracing both change and the status quo. 

Welcome, Pankaj, to the program. I’m really fascinated by this issue of human-agent teaming and have worked on it for a number of years. Let’s jump right into it. 

When you talk with people about the impact of AI on humanity and society, people tend to fall into two camps. You either have this Hollywood dystopian view that the robots are going to destroy the universe and everyone takes everyone’s job, or you’ve got the more utopian view that technology will make life better, lead to a more prosperous future, and let people do more meaningful work. 

Given, FortressIQ’s lofty mission to unlock the limitless potential of the global workforce by accelerating the responsible and ethical use of AI, it’s probably safe to assume that you see the more positive contribution of technology. Is there a darker side that we need to worry about?

Pankaj: It’s a great question. I think the thing to do is to step back and say, what would make AI different from anything else? Any technology that we as a human civilization or a more technology-advanced society, has always had positive and negative to it. 

If we think of just going back and looking at nuclear power, there’s a tremendous potential for both good and bad there. AI is really very much the same thing, that there are going to be incredible use cases of how AI is going to help us to do things. But like any technology, there’s going to be tremendous potential for abuse also. 

I think the real thing that’s changing is that the rate and pace at which AI is improving is something that’s been relatively unprecedented. The questions that we have to ask ourselves are changing completely. It’s not when we talk about jobs being shuffled amongst different categories. When you start talking about AI, you start talking about not jobs and just in these tests, but in an entire category of work that is going to be reimagined. I think that’s where the infrastructure of how to even ask those questions needs to be further explored.

Jon: That’s great. I think there’s also the issue that sometimes with AI and technology, you think of it as a darker side, but it’s really almost an unintended consequence sometimes. It wasn’t intentionally put in there as being negative. But based on the data and the other aspects of it, it can seem to be a negative outcome. That goes along the lines of people like to think in terms of absolutes. As you say, it’s either AI’s good or AI’s bad. It makes the decision easier to make. The answer is either all technology or no technology.

In your experience, what’s the preferred partnership of this AI-human integration look like, specifically around delivering successful transformation? What’s the ideal blend?

Pankaj: I don’t know if there’s an ideal blend that is just a good rule to use everywhere. It’s different for different use cases, for the maturity level of both the function and the AI that’s trying to help with that. I think it’s one of the things that we have to look at as a notion and the fundamental understanding that a joint solution is always going to be better.

When we think about team sports, the idea that there’s no I in team is something that you learn very, very young the idea that you can’t be an individual player in a team sport, you’re never going to win.

When we’re looking at transformation and the reimagining of these job roles, looking at it through that same lens of you wouldn’t expect someone to do a jobmany different types of jobswithout a laptop. You’re going to get hired. You’re going to be given a laptop so that you can do A, B, or C. We wouldn’t expect people to do certain roles without a pen and paper. 

AI is kind of that same level. There is going to be a certain level of interaction with artificial intelligence that every job role is going to have. The key is just to figure out how much of it works and if it actually adds value. Then, making sure that it gets aligned there.

Jon: I think that makes a lot of sense. It’s really that augmentation of the workforce that I think AI can come in and help out with. They need to play off of each other, let AI take care of the lower-level task work, and let the human come in and take care of the more pieces that require more thought and more intellect on it. 

Building on this idea that technology and humans need to work together to be successful, you’ve written in and talked about the four realities of transformation, which I think are on point. I’d like to explore each of them in some detail. The first one that you’ve noted, reality number one is your processes aren’t going away.

Pankaj: When an organization approaches transformation, there’s just this reality of stepping away from the four realities that I outlined, which are just cosmic reality that we have to deal with. One is this dimension of time. You’re not going to be able to transition something instantaneously. Even if you have a beautifully designed architecture that you want to get to, you’ve got customers, vendors, partners, employees that you’re working with today, that are going to need a transition plan to get there.

Transformation is not this idea. There’s a binary event. There’s a big bang. It’s change management and all these things that go into that.

When I say that your processes aren’t going away, what I’m trying to make sure that people understand is that the process is going to exist. The process of you need to ship something to your customer, your customer needs to be able to request information about a support or a return. Those processes are still going to exist. Many of the characteristics of those processes are going to need to be static over a transition period.

When you’re looking at how you can leverage technology, the idea of understanding that it’s not a big bang, that you’re going to have to be measured in your transition to that is going to be key to being successful because big bang is usually the one that goes boom, right? That you work three years on something and in the end you’ve got something that no one wants or was designed improperly. That iterative process of making it better and the incremental pace is what we see a lot more of, then try to adopt the more of an agile methodology towards it.

Jon: That’s why I think one of the reasons why you see so much struggle for successfully executing transformation programs, like McKinsey got it at 30% percent. I’ve seen other reports as low as 12%–13%. It is that trouble getting off the ground or those false starts that companies experience, so totally understand it. 

Reality number two that you talk about is your IT systems aren’t going away.

Pankej: Yeah, and that gets to the heart of that beating infrastructure of an enterprise. We’ve got far-flung ERP systems, workgroup-level applications, reporting systems that are delivering for us today. Any strategy that you have for transformation is going to have to figure out how to work with that technology in certain areas, obviously upgrade or replace it in others. I think that’s one of the reasons why you see technology like RPA in these things that can extend the investment of existing technology being so popular right now. It really does shine a light on this idea of how we get the most out of our existing investments.

When we talk about your transformation strategy, you have to deal with the realities of what your IT landscape is. It’s really being intellectually honest about, are you going to be able to move X, Y, or Z functionality to a new system? Would you be able to do it in the right time frame? Is it going to have the feature set that’s necessary? Really, building a transformation strategy grounded in that reality of what your technology landscape looks like today, and again, incrementally saying here’s how we’re going to move it to this better outcome. 

It highlights the challenge that’s been laid out by Clayton Christensen, the Innovator’s Dilemma. How much are you going to invest in your run the business versus the overall transformation of something new?

That balance is where it gets extremely challenging for the transformation professionals in the sense that we all know that we need to get off of a system A, B, or C, there’s going to be a multi-year transition to get off of that system, we can’t stay still while we’re doing it, and how do we make all of those realities work together?

Jon: We’ve got in through one and two. Your processes aren’t going away, your IT systems aren’t going away. The third one, reality number three, your people aren’t going away.

Pankaj: Yeah. This really gets to that point of any transformation strategy has to embrace the people that are going to be impacted by it. That’s why change management is so important as part of an overall transformation strategy. 

Even more so, the idea that because these people are oftentimes the core of what’s going on in the process, making sure that the transformation program is human-centric, in the sense of what can be done best by humans inside of a process, work in AI, automation, or anything can be leveraged to make that experience better, then building this program that: (a) understands where those frustrations are and where the technology can be leveraged be, (b) in any scenario where you’re impacting people, you want to make sure that you’ve got a transition strategy to either retrain, reposition the existing resources to make sure that you’re really building a program that’s going to accelerate and be embraced across the entire organization.

Jon: We’ve hit on the classic golden triangle of people, process, and technology. The last one, reality number four, your organization’s structure isn’t going away.

Pankaj: That’s one that we’ve seen a lot of challenges with the transformation with AI, with RPA, is where some of these initiatives are going to sit. What should we have that’s IT-led? What should we have that’s business-led? And how are we going to work across shared services if there’s a technology-shared service and application-shared service?

Any transformation strategy that requires a wholesale change of the org chart on day one is going to be most likely a non-starter. Looking at the org structure and saying how do you leverage all of these different components to really make sure that you’ve got a program that can be adopted by all these key stakeholders? You’re going to have compliance, security, training, HR. All these things need to be really taken in so that you can make sure that the organization that you’re building and that you’re actually trying to get to has a very, very clear path of how the existing organization is going to arrive there.

Jon: Thanks for walking us through those four realities. That framework and approach for guiding transformational success is very helpful and I think a great point to end on. We’ll make sure to provide a link in the show notes to the original article for anyone interested in exploring the model in more detail. 

To recap today’s conversation with Pankaj Chowdhry, founder and CEO at FortressIQ—certainly one of the pioneers and market leaders in the emerging process intelligence space, really helping companies gain operational insight to drive strategic business initiatives—we got great insight on how organizations on the transformation journey should take this somewhat counterintuitive approach of embracing both change and the status quo. The key to the speed and success of a transformation program really lies in figuring out how to leverage the components of the existing while delivering that desired change and getting to that magical future state. 

Pankaj, thanks for joining me today. I want to give you an opportunity to make any closing comments or provide any final insights. I also have one final question for you. I’m a bit of an information junkie and always looking for the latest and greatest resources. My final question to you is, what resource, website, newsletter, podcast, framework—whatever it maybe—do you rely on to be successful and knowledgeable in your role? 

Pankaj: I read a lot of arXiv sanity. There’s a website that a lot of people are open-publishing there. They’re academic papers, and they’re the latest and greatest that they’re working on. They exist outside the traditional scientific publication channel. You see all of the greatest uses for technology and more specifically, AI. But you can get overloaded because there are new papers being published every day. arXiv sanity really helps to just bubble up some of the most important papers that you need to read so that you can stay up-to-date on the quickly moving pace of technology these days.

Jon: That’s great. That’s one that I have not heard of. I look forward to checking it out. My resource in this episode that I came in with is the Farnam Street blog and podcast from Shane Parrish. It really, really helps out with accelerated learning by sharing the best of what people have already figured out. It’s got some great insights on a lot of different mental models across disciplines. 

We will be sure to put a link to all the resources recommended in the show notes. That’s a wrap on today’s show. Thank you, Pankaj, for joining me and for FortressIQ sponsorship. I’m Jon Knisley and this has been hello, Human.

Promo Teaser

Welcome to the “hello, Human” podcast, a show about AI solutions, advancements in technology, and about the leaders in the tech industry. In this teaser, host John Knisley talks about his background, an explanation of his philosophy on AI & how it can impact society, and what you can look forward to in the first season of Hello, Human.

Promo Teaser

00:00 / 00:02:51

This show is supported by FortressIQ, whose mission is to unlock the limitless potential of the global workforce by accelerating the responsible and ethical use of AI in the enterprise.

Subscribe now, wherever you find podcasts so you don’t miss any of the tips, strategies, or tales from the front lines of the submerging and rapidly evolving technology that is impacting how every one of us lives, works, and plays. 

Talking Points:

  • What is “hello, Human”?
  • What to expect from this podcast
  • What is our philosophy on technology
  • The story behind the name “hello, Human”
  • The formatting of episodes


Website: FortressIQ

Full Episode Transcript:

Welcome to the hello, Human. podcast where we are going to discuss the latest topics in artificial intelligence, and how it’s being applied in the real world. 

Subscribe now wherever you find podcasts so you don’t miss any of the tips, strategies, or tales from the front lines of this emerging and rapidly evolving technology that is impacting how every one of us lives, works, and plays. 

The show is supported by FortressIQ, whose mission is to unlock the limitless potential of the global workforce by accelerating the responsible and ethical use of AI in the enterprise. 

My name is Jon Knisley, and I’m your host for the hello, Human. podcast. I’ve been a technologist my entire career working at the intersection of business and IT bringing first-in-class technologies to some of the largest enterprises in the world. From a shelf-scanning application to verify product placement for a CPG company, to automatically assessing world quality for an automobile manufacturer, I’ve had a front-row seat to how emerging AI can deliver value for businesses of all shapes and sizes.

This season on hello, Human., we’re going to be talking with the leading builders, explorers, and warriors of AI solutions that are having an impact today, and making a difference in their companies or with their clients. Upcoming episodes tackle a framework for establishing a resilient digital core, tactics to guarantee privacy in the age of AI, when AI will finally disrupt biopharma, and planning for human-bot teaming. I can promise much, much more. 

Generally, hello, Human. will provide a more utopian view of technology, and the impact it can have on our work and society. We’re not Pollyannas, but we’ve been through enough cycles and around the block enough times to have developed a favorable view of what technology can accomplish. Because we’re biologically programmed to look for danger, it’s easy to fear what the future may hold. But we’ll leave the more dystopian view of technology to Hollywood, and explore the applied AI landscape broadly for its ability to advance humanity. 

If you’ve made it this far, you’re probably wondering why hello, Human? It’s a play on the common introduction to computer programming where a few lines of code is written to display “Hello, world” on the screen. But we’re focused on AI and improving the understanding of the opportunities presented by the technology. It’s hello, Human. to help jumpstart an intelligent conversation and share with you the leading voices in AI today. 

Episodes will run approximately 20 minutes and will be released every two weeks. We look forward to sharing our guests’ knowledge and insights with you and welcome your feedback and recommendations anytime. Thanks again to FortressIQ for sponsoring the program. Subscribe today anywhere you listen to podcasts. Welcome, and thanks for listening.