In this special edition episode of the Futurum Tech Podcast, Daniel Newman takes a look at the concept of the cognitive enterprise—the effort to bring new technologies and operational models into one’s enterprise as part of digital transformation with interviewee Gene Chao, Vice President and General Manager of IBM Automation. In his work, Gene manages IBM’s global automation business unit, covering software, platforms, and services both to outside clients and IBM. Thanks to IBM for sponsoring this important episode.
The cognitive era is all about rethinking how humans and machines relate—how we structure our people and assets to thrive in a digital working environment—and how to build those environments so that they are scalable and valuable. My guest believes that coexistence of humans and machines is key—and I agree. All new models, workflows, and processes need to keep the joint work of people, systems, and algorithms in mind. This requires looking at our business processes in radical new ways. Democratizing tech—layering it intelligently through an organization—is essential in today’s marketplace. But it takes work.
Still, just like we’ve seen a continuous transformation from on-premise data centers to cloud to multi- and hybrid-cloud environments, we’re seeing companies grappling with the need to think—and rethink—the way they’re structuring their tech and people. Gene stressed that there is no single panacea that will solve a company’s problems. Likewise, there is no plug-and-play RPA or AI that will jive perfectly with your company without training and development. Every business will need its own automation roadmap. With all this in mind, we discussed how IBM is changing the way tech and humans work together.
With a “put smart to work” motto, IBM creates literal job descriptions for the tech they adopt with a goal of building a new digital workforce that works seamlessly with human employees. In order to experience the type of success IBM has seen in that effort, companies also need a culture to accept and foster it.
Digital transformation today requires looking not just at technology but how one must change their labor and operating models to support it. There are some companies moving from the tip of the fear of foundational automation to more mature levels—seeing it scale meaningfully without the enterprise. In the end, you can have automation in a company without it being a cognitive enterprise. However, you can’t have a cognitive enterprise without automation.
Interested in changing your business model to become a cognitive enterprise? Read the seven steps IBM outlines to get there.
Daniel Newman: Welcome to the Futurum Tech Podcast, Interview Series. I’m Daniel Newman, Principal Analyst at Futurum Research and your host for this episode. This episode is in fact sponsored by IBM and we’re really excited to have Gene Chao, Vice President and General Manager of IBM Automation joining us.
Now, for those of you that have listened to all of our podcasts, you may remember that Gene has joined us before and we’re excited to have him back on the show where we will be talking about the cognitive era and the cognitive enterprise, but before we jump into this interview and discussion with Gene, I wanted to say hello, welcome you back to the show, and give you a chance to just quickly introduce yourself and tell everybody out there just a little bit about Gene.
Gene Chao: Hey Daniel. Thanks again, great to hear your voice and always good to talk to you and appreciate the forum here. Just a reminder for our listeners, what I do for IBM, I lead our IBM automation business unit. That business unit globally comprises everything from our software components to our platform, our services orchestration platform, across to our services and fees, which include things from consulting and advisory, across to our run and operate surfaces. So, we’ve become a pretty comprehensive business unit or operating unit within IBM for our clients, but also we’re a service provider to IBM. So, pretty collective and comprehensive view of automation and AI and all the neat things we’ll talk about today.
Daniel Newman: Oh absolutely. Well, I’m so glad to have you back, really always enjoyed having you on the show. Things are moving fast out there and the topic itself, cognitive era and cognitive enterprise, two big words top, you know, somewhat trendy, buzzy topics that people are really going on about, but they’re very important and very meaningful and I’d love to get your take. When you say cognitive era and cognitive enterprise, what do you mean?
Gene Chao: Yeah, it’s a great question and it’s interesting because we always talk about these large phrases or words, right? We heard about AI, now you have cognitive, and now we even introduce things like the word platform. These things could mean a lot of different things to a lot of people, but what we talk about in here, how we talk about it here, what we mean by that here at IBM, Daniel, is essentially how do we take advantage of the new technologies?
How do you apply them in the business models and the operational models around these enterprises? That could mean certainly in and out of IT, but also in terms of both core and non core functional areas of our client’s businesses. By the way, we do that for ourselves. So, in a nutshell is, how do we advance and apply these new technologies that reshape how work gets done? Hopefully that makes sense.
Daniel Newman: Oh yeah, absolutely. I think there’s so much to this, you know, we’ve talked in the past about automation, but obviously automation is streaming itself into more full-blown AI cognitive, ushering in sort of this human machine era, right? Where humans and machines are working together to create exponential effectiveness that goes far beyond what either we’re able to do in isolation from one another. Which kind of brings me to that next question, you know, what do you see as the market opportunities and the benefits of companies kind of working towards becoming cognitive enterprises?
Gene Chao: It’s a great question and let’s start with our foundational premise here. You said something that is exactly right. You talked about humans plus, or humans and machines at a high level. Our foundational premise is very much on the topic of coexistence. There’s a lot of what you and I would call FUD factor out there. You know, that fear, uncertainty and doubt. Are robots going to take my job? Is the machine smarter than human intuition? We don’t believe it’s an or issue. It’s an, an, it’s a coexistence.
That coexistence comes across in things as simple as cloud environments. You have your infrastructure type of services, your software as a service. Those types of things become key drivers in terms of how these operating environments work and you have to understand sort of the self service, autonomous nature of those things across all the way certainly to what you and I’ve talked about before in terms of artificial intelligence.
Where are the new ways of understanding that intuition or type of reasoning? How do these workflows, and when I say workflows, I mean what people do, what systems do, and what algorithms do, how do they coexist? There’s that word again, to build scalable burstable capacity, whether that’s a labor effort or in fact how we kind of build these autonomous types of systems. So, the opportunities, if you will, the landscape that we’re looking at, is fundamentally a reshaping of these environments. It’s not a simple bolt on anymore. It is fundamentally reshaping how work gets done.
Daniel Newman: Oh, absolutely, and there’s so much talk about this right now, and I know you mentioned we’ve gone from an era of On-Prem or cloud, to hybrid, to multi-cloud. We’ve gone through an era of DevOps to AIOps, right, to basically right now really having to find a way to bring all of these technologies together and make them all available. So, it’s not data in isolation anymore.
It’s not using AI for one thing or another thing. It’s really having these capabilities layered throughout your organization so that everybody, you can almost democratize it so anybody can use it, right? It has to be simple at that level so people can use it without a ton of technological capabilities, but concurrently the architecture to do this takes a ton of work.
Gene Chao: Sure. For sure, and there’s a few things in what you’re saying that we see not only at a trend level, but an actual very tangible practitioner level. A couple things you mentioned, the first one is the shift from DevOps into AIOps. Absolutely right. In that, let me say that a different way. DevOps was sort of the machination or a collision of what’s happening at a software code development versus what the operational part of the business wanted, right, but even the word mashed together, development and operations, that movement to AIOps was a more intelligent system.
The need to not only train it, but see what the opportunity was to have a unsupervised type of training, and there’s a term that is used in DevOps called CICD, or continuous improvement continuous development, never more than now has that been more true with those two types of operation development worlds colliding. That coexistence is driven from that CICD methodology. Again, that’s the collision point in the technology in the human beings. So, you’re spot on with some of that stuff.
Daniel Newman: Oh, absolutely. So let’s go back though, because automation is an area that you’re very focused on. How are you looking at the roadmap that companies should take? Because for instance, automation is sort of always seen as sort of the base or the tip of the iceberg in terms of cognitive, but it’s critical and when you describe the role it’s supposed to play, how do you do that?
Gene Chao: Look, as I always said, the automation componantry is foundational, right? It’s not only about the task automation that the departure point of automation was screen scraping and task level activities being done by some algorithms and scripts. We’ve moved from there into full job role descriptions of the so called bots. Inside of IBM we have over 28,000 digital workers and those digital workers, so called bots, have job descriptions. They have a starting point and an end point in terms of what capabilities they have and we treat them almost like a human employee.
So, when I say foundational, it’s very much rooted in not only tests and activities, but job charters and functions of the company. Those types of things have to get integrated into what I’ll call an intelligent type of workflow, and we keep using these phrases, right? The word cognitive or the word intelligent, all that means is, we have a great marketing line right now is, put smart to work, but that smart needs to have a start point, right? Is the start point the human being, being smartest starting point or are we talking about machine learning? The answer is yes and yes.
So, foundationally speaking, we’re changing the way our workflows happen. We’re changing the way we look at how processes are designed and that even has an economic or financial impact at the back end of it. So, it continues to be more and more important. However, and I’ll end with this, the notion that it’s a simple plugin play and a bot runs in perpetuity, leave it alone, that’s really not the way to look at it. Just like a human being you have to foster it and nurture it. You have to make sure you develop it and train it. There’s a new wave of how to basically foster these new enterprises.
Daniel Newman: Yeah, you can see that and I think you’ve definitely made the point. It kind of has to start. You have to be able to drive in these business models of productivity, but you also have to drive the culture and I think automation is a really great starting point for building the culture that’s going to be able to embrace those next waves of technology because it kind of has to start somewhere. People have to see it work, in any digital transformation it’s not just going to be founded in the technologies that are applied, but also the organization and the culture and how it reacts to the change and how enabled it to support that change, which is essentially automation is something that people can see.
They can feel its results and its impact. They can experience it in the business and then ideally they build more confidence in greater capabilities that a cognitive enterprise has, and I think I probably know the answer to this question, but I want to ask you anyway. Do you think companies can skip past automation? Can you be a cognitive without automation? Is that even a possibility or plausibility at this point in time?
Gene Chao: It’s an interesting question. My lens is, can you be truly cognitive without automation? I think the answer is no. Part of the reason is those intelligent workflows, those new ways of working, really have to be driven from a digital era. When I say digital era, there’s another catch phrase, right? I mean machine time, I mean aspects of the software, defined labor units, all of these things, intelligence and our reporting and true AI machine learning, all those things have to be in there in order to be cognitive.
Now, on the other side, or conversely, can you have automation without being cognitive? Absolutely. You know, we all grew up in sort of a Microsoft macro world or screen scraping or task-based type of automation. You don’t have to be entirely cognitive there, but if you’re going to get to be a cognitive enterprise, you must have an automation element inside that model for sure.
Now, the second part of what I want to say is, you also bring up a good point. There is a distinct difference between visible and invisible type of automation and technologies. I’m still struggling with what human beings are more comfortable with. Are we better off or more comfortable not seeing it work, or is the new system of engagement that you see, feel, engage with that, whether it’s a Siri type of technology or watching a bot work, does that make us more comfortable? That’s kind of the the balancing act that we’re going through right now, but to answer your question, you have to have automation elements in your cognitive journey, otherwise you’re not cognitive.
Daniel Newman: Yeah, I think that that does make sense, and automation is sometimes not seen in itself as cognitive because it’s seen more as programmable, script driven, but on the other end, I think, cognitive is sort of what flows from automation. Like I said, I really kind of think it’s the tip of the spear. I know I’ve said that a couple of times, Gene, but I really do think for a lot of companies it is the first real experience of leveraging the power of a AI driven, cognitive driven enterprise, and it starts with automation and so many people really don’t discern the difference, by the way, when they’re experiencing something that’s done through automation or AI.
That’s the stuff that you know and I know an engineers might know, but then a lot of times when it comes to driving an experience, automation can actually serve and accomplish a lot based upon the way it’s programmed. So, let’s talk forward about some examples because I think we’ve talked a lot of philosophical here, Gene, we’ve talked a lot about sort of what it should look like, what it maybe shouldn’t look like, but people, I think, like to understand through the eyes of some clients and you have some impressive clients that have moved from sort of the beginning, or the tip as I like to say, and now they’re getting into much more mature levels and really becoming cognitive enterprises. Can you share any insights there?
Gene Chao: Yeah, it’s funny because let me kind of put a capstone on the last part of our conversation and this kind of is a good segue into the types of clients or use cases. I use sort of a crude example about, can you be cognitive without automation and vice versa. Think about the way we drive our cars, right? We have speed control or cruise control on the car. It’s kind of a dummy way to say, “Hey, that’s automated, keep the car going at 50 miles an hour.”
There’s a distinct difference between that and autonomous vehicles delivering your Domino’s pizza, right? Ford and Domino’s put that use case together. They have driverless cars delivering pizza. It’s all based on similar technologies, but one is truly cognitive to find a place to deliver a product, and the other one is just maintaining my speed and keeping me out of trouble.
You know, those are the types of things that we look at all the time and that brings together a lot of different use cases. I still find there is a dichotomy in terms of automation. One is very physical, and the other one is very much oriented around thought workers. The physical aspects of thinking about the robotic arms, assembly lines, things like that, I mentioned the autonomous vehicles, there is an internet of things aspect to that. That’s probably a whole other podcast we got to get to. What we’ve been focused on is the thought worker elements of the cognitive enterprise. The biggest use case, and I want to be careful because it’s not sort of a shining light on ourselves, but IBM is my biggest use case. We have fully adopted in our finance, IT, procurement, HR areas, how to take on this journey as a cognitive enterprise.
We’re using elements of our AI engine, as you know, called Watson, our automation scripts, our orchestration platforms and we have pretty significant operational gains across those functional areas that I talked about. We have other clients that are going through touchless supply chain operations, you know, companies as big as Kraft Heinz and Nike and those types of folks. While I can’t talk specifically about what they’re doing, because it’s proprietary, they have actually advanced the ball down the field in terms of, how do I look at a digital workforce or software based labor model, how do I redefine my process and the workflow strains and how do I change my labor model and my economic model? So, at a consumer product industry level, it’s one of the biggest investors in the space. It’s only outweighed by the big financial institutions.
We’ve all heard about robo-advisors, we’ve all heard about how we stand up these bots that go after tax returns and things like that. Those are things that are here and now, they’re not futuristic. The futuristic part is, how do we tie them into an omnichannel experience, a full breadth or wider customer type of experience. So lots and lots of use cases emerging, Daniel.
Daniel Newman: Yeah, I can definitely see it’s always great to be the cobbler’s kid that actually gets shoes and it sounds to me like, you know, you’re sort of the living, breathing, walking example of a company that is doing it, is walking the walk and not just talking about it, and I think that’s really important. You know, everything from opportunities to shorten closing cycles at the end of a quarter, right, where you want to help financials go from spending weeks ahead of an earnings report, right? Working 14 and 16 hours a day to try to catch up and get everything done to close the books, and with combination of automation, AIOps, you’re seeing companies getting that down to the press of a few buttons, and of course there’s a lot of work that goes into that.
So, nobody wants to set the the bar that you buy some automation and you’re two clicks away from getting all, but if the process is embraced, if it’s put into place, if it’s well understood, documented, and then figured out, there is legitimately ways to take hours and hours and days of work and put it into minutes for certain tasks that can be very overwhelming and very consuming. I think that’s why these big companies are so on board. It’s efficiency, it’s speed, it’s time to value, all those catchy, buzzy, digital transformation terms become very real and very visible with what automation can do when you put it behind powering some of these tasks that are so mundane, so repetitive, and so large and it sounds to me like you’re doing it at work and you’re doing it for some very large customers, Gene.
Gene Chao: Yeah, for sure, couldn’t have said that better. You know, there’s more to come and we’re looking forward to the next series of problem sets we’re going to apply these technologies against, and they’re becoming much closer to what I say our client’s customers, right? So, right, moving from enterprise to the end user.
Daniel Newman: So let me ask you one last question, kind of the big interview question. It’s a chance to promote without promoting, because nobody really wants to listen to a podcast talking all about any promotional item, but at the same time, everybody that’s listening to this is probably going, “Okay, how does this work for me?” So, you’ve done this enough now to kind of know when a companies going through the process of trying to become more of a cognitive enterprise, trying to embrace automation, what’s sort of the advice from your experience, from seeing it being done, that you’d give to a customer and say, “Hey, start here.”
Gene Chao: First and foremost, and I appreciate that question and I’m not here to sell IBM or anything else either, right, so it’s just about just the point of view and an approach. First and foremost, there is no single technical thread that is the panacea of solving these challenges. Some people have said, “Hey, if I put an RPA in that’s,”… RPA’s are fantastic, but it’s not going to solve the entire workflow problem or putting in a really smart AI element, machine learning, that’s great as well, but you’re going to have to start threading these things together and decide where the orchestration, or what I call the fabric, resides.
Yes, can you run IT? Yes, can you run accounting, but you have to start looking at your labor and your operating model and that’s hard work and there’s not an easy button to hit that says, “I’ve solved it, put it in place and run it,” sort of the working thread that we have is, we’ve moved from a on demand or instant on economy to an available anywhere, right? Everything has to work all the time, 24 seven, so we’ve moved from that now into, where can you source these things? Are they in fact available anytime, but also anywhere, right, on your mobile device, in your car, on your laptop. It’s expanding pretty quickly, so understand your echo system of technologies, how to put the fabric together, and where to apply it. That’s my best advice.
Daniel Newman: Absolutely, I think that’s a great start. Gene Chao, IBM’s GM and VP of their automation group, I want to thank you so much for taking some time with me on the Futurum Tech Podcast interview series. I think this is a super interesting topic. It’s going to continue to evolve. I wrote an entire book about it along with Olivier Blanchard called Human Machine, so it was certainly a topic that we believe very, very strongly in and we look forward to this conversation continuing to evolve.
For everyone out there, make sure you check out Gene in the show notes. We do have a link for those of you that do want to find out more. IBM, thank you very much for your support and for sponsoring for Futurum Tech Podcast and this episode. Thanks everyone for sitting in and tuning in and we’ll see you all very soon.
Thank you for joining us on this week’s Futurum Tech Podcast, The Interview Series. You can listen to the full podcast by way of your favorite streaming app, simply by clicking one of the links below. And if you’ve not yet subscribed to the Futurum Tech Podcast, let’s fix that!
Disclaimer: The Futurum Tech Podcast is for information and entertainment purposes only. Over the course of this podcast, we may talk about companies that are publicly traded and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors and we do not ask that you treat us as such.
Daniel Newman is the Principal Analyst of Futurum Research and the CEO of Broadsuite Media Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise. Read Full Bio