On this episode of the Futurum Tech Webcast – Interview Series, I am joined by Don Schuerman, CTO at Pega. Our conversation takes a look at the AI industry and how organizations can expect to incorporate AI into their business practices in the future for better customer experiences.
In our conversation, we discussed the following:
- Misconceptions about the utility of AI
- Avenues for implementation, optimization, and efficiency
- Maintaining adaptability in the face of societal and economic changes
- Fostering an informative partnership between AI and its human users
- Implications for the future of AI accessibility
It was a great conversation on a timely topic, and one you won’t want to miss. To learn more about Pega, check out their website here.
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Disclaimer: The Futurum Tech Webcast is for information and entertainment purposes only. Over the course of this webcast, 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.
Transcript:
Daniel Newman: Hey, everyone. Welcome back to another episode of the Futurum Tech Podcast. I’m your host, Daniel Newman, founding partner, principal analyst at Futurum Research. Excited for this interview series talking about something near and dear to my heart, artificial intelligence. It’s something that’s probably even more near and dear to the heart of our guest, Don Schuerman from Pega, who by the way, has been on the show before. I’m very excited to have him back and I’m really appreciative of Pega for being part of the show this week. So without further ado, Don, welcome back to the show. How are you doing?
Don Schuerman: Yeah, I’m great. It is good to be back.
Daniel Newman: You know what? That’s a great answer. It is good to be back. It’s actually funny. You and I were talking in the green room a little bit. We’re back and yet we’re still in a lot of ways talking about the same things. You gave a great keynote. I can’t remember if it was four years ago, three, five. The COVID year created this blur in my mind and I’ve tried to push them down except for the good moments, but at the same time, I still remember you doing this whole process of walking us through the people in our life that we know best, talking about your parents and your siblings. I think then you did something about your spouse and then you shocked everybody in the room when you explained that Facebook knew you exponentially better than your spouse does. It was all about our behavior online, what we search, what we do, and how advanced analytics, machine learning, deep neural networks, and AI are going to change our lives.
I want to talk to you about that today, but I guess before I do, I know CTO at Pega, a very cool, very exciting company, just give everyone the quick like, “Hey, I haven’t listened to Don in a week.” You need a reminder of who you are, what you do, and just a quick background on Pega for those not familiar.
Don Schuerman: Yeah. I have the honor of being CTO of Pega. I explain that as meaning that myself, my team, we spend about 50% of our time with our clients, making sure that we really understand what their needs are, where they’re going with technology, where they’re going with their business, how those two intersect. We spend the other 50% of our time with our product and strategy teams, making sure that what we’re picking up from our clients ends up being reflected back on our products and our go-to-market strategy and how we tell our story.
It’s nice because I get to work across technology, across client-facing functions, across marketing functions, get left brain, right brain all activating and firing at once. Pega, our technology is a low-code platform for AI power decisioning and workflow automation. So how do we use AI? How do we use automation? How do we put those capabilities in the hands of both IT and business users through low-code?
We work with large clients across the globe to apply that technology to how they personalize the engagement with their customers, how they accelerate the acquisition and onboarding of new customers, new partners, how they automate their customer service. Underneath every customer service experience is a bunch of decisions and a great workflow. We really drive a lot of that customer service automation, how they optimize operations from the core operations that actually build the products they bring forward to their customers, and that finally, how they resolve exceptions, how they fix things when they go wrong because that’s often time the real moments of truth in some of the most complicated pieces of work they need to do.
Daniel Newman: Yeah, no, absolutely. There’s a lot there and it’s a big job. You really alluded to something that has become paramount and one of the most critical things that companies can consider right now. One is automation and then two is driving a customer experience, which really is the core of the Pega business, at least as we as analysts have looked at the marketplace. I’d like to maybe take a moment and I guess take a step back. As an analyst firm that works with over 150 of the world’s most exciting and prolific companies, we take AI for granted as something that’s just talked about every day, every company. It doesn’t matter if it’s on an edge device, on our computer, on an analytics or software platform, and we just ubiquitously say AI. My recent Forbes top digital transformation trends, I said AI becomes ubiquitous even more so this year with everything, but there’s a lot that goes to that.
Then by the way, there’s a lot that therefore is slowing the implementations of AI at scale in the enterprise. So you being at the core of this, what’s your interpretations of what’s holding organizations back from really going all in on AI right now?
Don Schuerman: Yeah. I think it starts maybe with you’ve got to get to the next level of understanding of this. I think for better or worse, those of us in the software industry, largely because AI is exciting and cool and new, but we’ve created this world where AI is just this big futuristic blob of stuff when really, if you take AI and you break it down, it’s actually a bunch of very specific technologies which are very different, frankly, and are uniquely focused at solving very specific problems, that the AI engine that you might use to interpret a customer’s voice and make real-time recommendations to a customer service agent around what they’re asking for is actually different than the kind of AI you might use to recommend the next best action to a customer across all kinds of different channels and different interactions.
They actually operate on different value levers, different value deliveries back to the business. I think the important thing for organizations is you think about going from AI as this really exciting thing to the way I like to look at it as a pragmatic tool that you can use to solve real problems and create real opportunities in your business is getting down to that next level of detail of what’s the business need I’m trying to address and what’s the specific tool out of the AI toolbox that I can then apply against that business need.
Daniel Newman: As the CTO, I know you’re a real hands-on type of leader, you work internally on the product and you also are the type of leader that likes to get in front of your customers and hear them out, what do they need and then try to figure out how to build it, right?
Don Schuerman: Yeah.
Daniel Newman: What are you seeing right now in terms of the typical use cases, meaning as these companies are putting toes, feet, legs into the water, where does it start? How does it proliferate?
Don Schuerman: I think there are a couple of big areas where we see clients applying artificial intelligence technologies. Maybe I’m oversimplifying this, but in many ways, we apply technology in the business, we apply it so either a business can either grow more or save money. That’s where we apply technology. In that first area, we see a lot of clients using AI to hyper-personalize their interactions with their customers, moving away from this mode where we just sent everybody the same email, we blasted out on the email list, everything was ignored anyway to every time I have a conversation with the customer, can I figure out in real time what’s going to be the most relevant thing to talk to them about? The really smart companies understand that that’s not always an offer. That’s not always, can I sell you something? That might be, “Hey, I know you have a support issue open. Let me give you an update on that.” That might be, “Hey, I understand you’re going through a period of financial hardship. What can we put in place to help you and assist you at this time?”
It might frankly be don’t say anything and just let the customer ask you what they want to ask you, but using AI to predict that, to look at historical data of other interactions and figure out in real time what’s the most relevant conversation to the customer and then balance that in real time with what’s the most valuable thing for the business, what’s going to be the thing that creates opportunity for the business to either sell new product, retain a customer, deliver service more efficiently, balancing that in real time so that every conversation is optimized. If you apply AI I think correctly here, you’re not doing that based on a batched up AI decision that you made the night before. You’re actually doing that in real time so that every click on the website, every piece of information the customer gives to a service agent is actually redecisioning those pieces of information so you’re getting more and more relevant to the customer as you grow.
So that’s one big area. We’ve seen organizations like Commonwealth Bank of Australia or Vodafone in the UK drive hundreds of millions of dollars of benefit to their business while being more relevant and responsive to their customers. The-
Daniel Newman: Yeah. Go ahead. Go ahead.
Don Schuerman: I was going to say the other big area is more around using AI to drive efficiency. We have lots and lots of processes that are automated in the business and there’s, I think, a huge opportunity to use AI to find places where we can make those processes even more efficient, predict, for example, when we might miss a deadline or a service level agreement, predict that we’ve started a process that we know at the end of it, you’re going to end up writing off. Well, if I’m going to end up writing off this interaction, why don’t I just do that at the beginning before I spend a bunch of my customers’ time and my employees’ time researching it and tracking it down. That kind of using AI to find optimization in the process is another huge use case that we’re seeing.
Daniel Newman: Yeah. No, absolutely. You brought up two things that I think are timely for our conversation. We were speaking a little bit in the green room, you and I, about how we like when growth is a little more in focus and it’s a little frustrating that the general market sentiment right now is so, I guess, down. I don’t want to say we’re out, but we’re definitely down right now. We had a raging few years of growth. The pandemic created this five, 10X accelerator of adoption of technology and now we’re having a comeback to earth moment as we’re try to figuring out what’s next. I had a conversation with one of your colleagues, Don, or a peer as I’d say, Bill McDermott at ServiceNow and you guys, some of what you do is similar, some of it is different, but we had this concept of teching our way out.
We basically said as we exit, and so you brought up two things in your last comment, but basically, companies that are going to stay above the fold are going to accelerate at CX, meaning they’re going to deliver the best experiences and they’re going to separate themselves from the pack or and/or they’re going to automate as many workflows as possible to try to reduce cost, churn, frictions, things that basically right now, maybe because we’ve been fat and happy for a little while, that we’ve maybe been a little lazy to get rid of unnecessary processes and costs. How are you thinking about the role of AI in the economy? Do you think there’s a possibility that the growth could actually accelerate as companies are going to look to use this to stay afloat while concurrently probably finding ways to rid other costs? Will tech stay above water in your mind?
Don Schuerman: Yeah, I think absolutely, I think especially if it’s tech that can be pragmatically applied. I think we do start to hit these boundaries where in some places, the hype of the tech has actually gone past the value it can deliver. We’ve got to be focused on where does it actually work, where does it actually deliver value. I think there are two things that I would say about where I think organizations need to go in this economy. I think there’s this interesting point where if you’re smart about it, the most efficient thing for the business can often be also the best experience for the customer. If I’m truly rethinking my processes and my experiences from a digital in kind of way, I’m building things that are not only more efficient for the business to run, but they’re also easier for the customer to do. I’m taking friction out of the customer experience, so I’m making my customers happier, but I’m also making my business more efficient.
I think when you think about how we tech our way out, I think there’s a third aspect that’s really important and that’s agility and the speed to change. I think one of the things that happened in the first wave of digital transformation when maybe we were all a little bit too excited running around tacking mobile apps and chatbots onto what are really just broken legacy processes, I think there was this sort of thing that, well, digital transformation is a race and there’s a finish line and once we get through our digital transformation projects, we’ll all put our hands up and go like, “Yeah, we’ve digitally transformed. Off we go.” I don’t think that’s what digital transformation ever is or was. I think digital transformation is about accepting the fact that we are now in a world of constant change. No one could have predicted COVID. No one could have predicted war in the Ukraine. No one could have predicted the continual hit of supply chain disruptions and economic concerns that we got hit with since the beginning of this year.
The organizations that will survive are the organizations who have built the kind of agility through digital platforms, through low-code software, through self-learning AI models that adjust in real time to respond as fast as possible to what I think are just going to be a continual set of pretty rapid changes to the markets that we’re in.
Daniel Newman: Yeah, I think that’s a great answer. I spend a lot of time dwelling on this. Of course, there’s different organizations applying AI differently to work across the entire continuum of a business process. Yours is very customer process-focused, very in the CX contact center, communications part of helping get to these next best decisions. Of course, there’s parts that have to deal with supply chains and then there’s parts that have to deal with all that nasty ERP data and then there’s human resource and all these things that’ll benefit. I’m curious, in your mind, Don, how does this go forward in the future? I siloed it there a little bit, but it’s got to become ubiquitous. We’ve got to start to tie these threads together. You might have point solutions, but they have to interact.
Don Schuerman: Yeah. I mean the North Star that we’ve been talking to a lot of our clients about is this idea of an autonomous enterprise. What does it mean to become an enterprise across how you engage with your customers, how you serve your customers, how you run your operations? You’re continuously optimizing the decisions that you make so that you’re making them faster, you’re making them better, you’re making them in ways that drive more optimal customer experiences, more better servicing, better operations. I think it’s worth using the metaphor of a self-driving car to think about how we’re going to get there. I’m a technologist at heart, but I personally believe that we are probably many more years away than people think from actually getting into a car, giving it an address, sitting back and reading a book while the car gets you there. I think-
Daniel Newman: Can I interrupt and just make you a prediction? I think we’ll do it on drones before we’ll do it in cars, meaning I think there’s a high probability we’ll do it in a flying taxi that way before we’ll do it in a car.
Don Schuerman: That’s exactly right, because the existing infrastructure we have of roads and regulations and insurance, even if we could get the tech there, which by the way, I get it. I think it’s harder because it’s a system, but what we already have is we already have cars that are partly autonomous in many ways. My car, I can put it into cruise control and it’ll dynamically change its speed as the cars in front of me stop and slow down if I end up in traffic. My car will warn me if I’m drifting out of a lane and nudge me back into the lane that I’m in. It’ll warn me if I’m backing up too quickly and not seeing something.
So those autonomous controls that we can gradually add to the business that don’t take over from the human, but they make the humans doing the work, whether that’s a person in the operations team or a customer service agent or a marketer or a salesperson, make them more effective by both guiding and supporting and taking away things they don’t need to do, but also putting up the appropriate safety and guardrails to protect them against things they may be doing inadvertently. I think that’s how we drive towards that future, that North Star of having a truly autonomous business that’s adjusting and changing on the fly. It’s not by focusing on can we get the self-driving today, but how can we help the humans who are still going to have to have a stand hand on the steering wheel, how can we help them be better, more effective, and frankly, more focused on the road ahead, more focused on the customer who’s at the other end of the conversation.
Daniel Newman: Yeah, it’s a human in the loop. It’s interesting, I had the chance about a week ago to fly out to Cupertino and I visited a company called Plus. They’re actually building L2 plus all the way up the stack for trucks, for semi. The business model was multipronged, but it was driver satisfaction, meaning helping the driver to, if you’re on the road 10 hours, keep them alert, make good decisions on lane changes, keep speed managed so that they’re not in as much stop and start and deal with traffic and by the way, make the business healthier because you help the truck run more efficiently, burns less fuel. I was impressed because that’s not really the story we hear because in consumers, really, it’s a race to that L4, L5. We want cars and everyone’s doing it their way. I was immediately thinking, “What car is he driving?” when you were talking about… I’m like, does it have LiDAR? Does it have radar? Is it all just vision? Are you driving a Tesla? But yeah, because multi-sensing I think is better than single-sensing, but that’s another story to talk about for another day and not here.
But anyways, very interesting analogy because the human machine, and I wrote a book called Human/Machine, Don, by the way, and we actually came up to the thesis that it is really more of a partnership than… We’re not working in opposition. We’re working to say, “Hey, how do we help a customer at 11 o’clock at night get a real customer experience with an online retailer even though they’re not going to have the phone staffed per se? What could a chat bot do to make sure that it’s giving them 90%, 95% all the way pushing towards 100% of the same experience you might get when you talk to someone, maybe even better.” Those are the kinds of little problems though that we’re trying to solve and then you make the business more efficient or you can hire differently and use your dollars to hire people that help you design the next product instead of supporting the unhappy customer.
Don Schuerman: I think for a lot of this, there’s this human in the loop when we talk about next best action. There’s lots of powerful algorithms that are sitting under the covers. I could talk to you about use of neural networks and gradient boosting and how you learn over time and accelerate the learning and the optimization of these models. But in every single good implementation of next best action I’ve seen, there’s also a degree of business logic, business rules that are put in there by a human to go beyond what the AI can learn and represent what the brand actually stands for. I don’t need a bunch of AI models to tell me that when a customer is in collections, I shouldn’t be pitching them a new product.
I don’t need an AI model to do that. What I need is I need good business value and good business judgment that could actually put a thumb on the scale and say, “Yeah, my AI model may be saying this, but my business says we do that.” I think that balance of what can AI inform and what can humans inform, when you get that right, that’s where I think you get these really differentiating experiences. That’s where get the ability to simultaneously serve your customers but also drive efficiency and free humans to do things that are more value add.
Daniel Newman: Absolutely. We all know those kinds of stories of where AI is working, but not very well. By the way, the whole I just bought this and I only needed one, but I’m going to continue to get the same recommendations for the-
Don Schuerman: Exactly.
Daniel Newman: … next 12 months, that’s such a good example of where a human would only have had to look once to have been like, stop feeding them that ad, but the AI does not know and does not have…
Don Schuerman: And that’s why you need the AI, but you also just need a really simple human-authored business rule that says if somebody said no to this or if somebody bought this, stop showing it to them. I don’t need a complicated algorithm. I don’t need deep learning to figure that out. I just need an if then else statement.
Daniel Newman: Yes, the old if then else. Takes me back to my little and minuscule coding experiences that I actually know how to do those. So Don, as we wrap up this pod, thank you so much by the way, it’s been great having you here, I guess where do you see Pega fitting in to the AI future? We talk a lot, at least I do, about the chip makers like Nvidia and their role is very clear. Then, of course, we talk a lot about the cloud providers because they’re basically democratizing it. You as a software, how do you see shaping Pega’s narrative to the market about how you’ll shape the future of AI?
Don Schuerman: Yeah. Well, first and foremost, I think one of the big things that we want to do is we want to connect AI to business value. I don’t want to implement AI just to implement data models because it’s cool. I want to be able to use AI pretty pragmatically to improve my customer experience so that I can grow my profitability with those customers. I want to use AI to improve the efficiency of my process so that I can actually improve my margins. I want to use AI to assist my customer service agents by recognizing text and voice and using that dynamically to provide the inputs so that the customer service agent isn’t sitting there manually typing everything in as they go. What we’re focused on is providing these real proven and pragmatic uses of AI out to our customers, but also working with them to ensure that as we apply AI, we’re doing it in an ethical, empathetic and well governed way. I think it’s really important that we are putting the appropriate guardrails and tools in place to ensure that if I’m going to use AI to make decisions based on my customer…
You were referring to a keynote earlier and I think it actually might have been my colleague, Rob Walker, who was talking about some of that stuff. But the other thing I think that Rob was pointing out is that Facebook can identify things like, are you a part of a protected class even without that data? So it’s possible for us to have AI algorithms that might have bias towards gender, towards race, towards sexual orientation even if that’s not an input to the algorithm. That’s why we’ve invested in tools like bias testing and empathy scoring to make sure that as our customers and our clients roll AI out, they’re doing it in a way that balances their customers’ needs and their business needs, but always in a way that we believe is ethical and governed, because I think that’s also the way that you’re going to build trust and you’re going to get more and more people to realize that used appropriately, this will make customers’ lives better, this will make employees lives better, this will make businesses more effective.
Daniel Newman: Well, you said it there. I like that. By the way, I totally agree with you. Sometimes the ads I get served make me ask questions about my life that I didn’t know I needed to ask. Like medication that comes across, you’re like, why am I seeing this?
Don Schuerman: Yeah, exactly.
Daniel Newman: What does it think might be wrong with me? But who knows? Maybe it used computer vision to see that my beard was flaking and it’s recommending something for eczema. You know what I mean? That’s where it’s going to go though. I mean that is how we’re going and maybe it was, I can’t remember, I really feel like it’s you, I remember the keynote, but I do remember that quote line is “Facebook knows you better than you know yourself,” and that line just stuck with me as like, holy crap, and this was a few years ago. It’s definitely gotten worse or better, however you interpret that in terms of what these systems know. The models have gotten better. Large language models, conversational, everything’s gotten better, which is giving you so much power. We’ve seen how GPUs and accelerators have advanced and all this stuff is probably just like you’re like a kid in a candy store as a technology.
Don Schuerman: Yeah. Well, between processing power and also just the ubiquity of cloud services so that we can scale this stuff up pretty dynamically, we can process massive amounts of data, I think the other piece of this that we’ve seen is also so important is as much as possible, driving transparency into the use of this. I firmly believe that customers are more than happy to talk to a chatbot. I’m more than happy to ask Alexa to do things around the house, but I don’t need Alexa to pretend or convince me that there’s an actual human being on the other side. I know Alexa’s a bot. I’m fine with that. I know that when I interact with some companies, it’s a bot. I’m fine with that. I think sometimes when we get into trouble is when we try to obfuscate that. We try to create a bot that makes you think you’re talking to a person, because you know what? I’ll pretty much figure out pretty soon that I’m not.
Or when we make predictions or we suggest these ads and we can’t turn around and explain to you why we did. I think that that explainable and transparent nature is another thing that we’re going to continue to evolve and continue to need to look at as we move forward.
Daniel Newman: Don, I love that and that’s a great way to end here. I want to say thank you so much. Really appreciate it. It’s been great to follow your work. It’s exciting to see that there’s companies really focused on solving these problems. I feel really blessed at times that we get to speak to just companies that are really building stuff. I know maybe it’s a little pie in the sky to say changing the world, but I really do believe this tech is changing the world and AI has the potential to be unleashed for a lot of things. I’m hoping that it’s going to be companies unleashing it for good, whether that’s better experiences, better services, the removal of friction, and of course hopefully, keeping us safe and protected and all the other things that I think AI has the potential to do. Thank you for all you’re doing to take part in that.
Don Schuerman: No, it was a pleasure. It’s always a pleasure to chat.
Daniel Newman: So everyone, there you have it. Check out the show notes. We’ll put some links down there so you can learn more about Pega and what the company is doing in AI as well as its services along with some of our coverage blogs, analyst insights, and research related to CX, AI, and the future of the business. Hit that subscribe button. We love having you here on the Futurum Tech podcast. You’re the best, but for now, I got to go, so I’ll see you later.
Author Information
Daniel is the CEO of The Futurum 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.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
