Experience Strategy Podcast: How to Use AI as a Force Multiplier for your Experience

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Satyam Kantamneni is Managing Partner and Chief Experience Officer of UXReactor, former Managing Director of Product Design at Citrix and PayPal. He is the author of “User Experience Design: A Practical Playbook to Fuel Business Growth.” In this conversation, we talk to Satyam about AI as a force multiplier for companies who understand their customers’ needs.

Voiceover: [00:00:00] Welcome to the Experience Strategy Podcast, where we talk to customers and experts about how to create products and services that feel like time well spent. And now here are your hosts, experienced nerds, uh strategists, Dave Norton and Aransas Savas.

Aransas: Welcome to the Experience Strategy Podcast. I'm your host, Aransas Savas.

Dave: And I'm Dave Norton. And today we are joined by Satyam Kantamneni, who is the Chief Experience Officer of UX Reactor, which is one of the fastest growing specialized experience design firms in the us. Uh, Satyam is formerly from PayPal and Citrix, and the author of the. Really usable and really compelling book user experience design, a practical playbook to fuel business growth.

And our conversation [00:01:00] today, we're really talking to a Satyam about how companies can truly become a meaningful and valuable digital experience company. The role systems play in that evolution and the topic that's been on everyone's minds lately. What is the role of ai? How do we use it to delight our customers to add value and differentiation in ways that are truly meaningful to our customers?

Satyam, thank you for taking a break from all of your busy work to join us here today.

Satyam: Thank you, your answers. And Dave, uh, it's a privilege being with you.

Aransas: Well, I, I know this isn't what we had planned to talk about originally, but I don't know that there's really anything to talk about other than ai. It has been the biggest shift in technology and in so many companies ecosystems over the past year.

So let's dive in there from your seat where you sit, what are you [00:02:00] seeing happen that is both exciting and. Maybe a little scary.

Satyam: So let's start with the scary part first. Uh, and I think that AI is front I center of everyone. Again, I live in the Silicon Valley, so uh, I see it, uh, you know, play out every day. Uh, so the scary part is, uh, everyone is like, you know, goes to the, uh, You know, this is the change. This is everything that's gonna be, uh, and that, that's one school of thought there.

The other school of thought, uh, is, uh, oh, this is still very immature. There's a lot, uh, to go. I mean, the other news, recent news article was, you know, OpenAI is losing $700,000 a uh, a day, and, uh, they're losing customers. So again, OpenAI is gonna go bankrupt. So I think you see both spectrums of that. The bottom line is AI has shown to us the powers of technology.

Uh, and AI shows to us, uh, at least [00:03:00] in, and, and a lot of us, uh, as, as, uh, you know, average users, it has shown that, you know, hey, what it could do to kind of take, uh, large language models and then put it to better use, uh, and, and make it a fairly democratized, I mean, we, everyone can get a free account and everyone can leverage and, and, uh, use the power of that.

So that's the power of ai. Uh, on the bottom level, and it's only gonna get stronger, uh, as the models get, uh, uh, stronger. Uh, you see that, uh, a lot of the AI models are built on self-learning models and, and learning is, is actually, uh, is not as over, uh, as complicated as we make it to. I mean, if, if. One plus one is always two.

And that's what models are. Patterns and models are very similar to that. So anyway, long story short, AI is here. It, it's gonna stay. Uh, however, AI, uh, how to harness the power of ai, just like any other technology that's kind of come out before that, whether that is meta, whether that is. Virtual reality, whether that is [00:04:00] mobile.

You just have to still understand what problems are we trying to solve and how do we make our lives easier with it? Uh, and I think AI is just a powerful technology stack that's available for all of us.

Aransas: Yeah, I think right now we're in a little bit of a, just because we can, we will phase and, uh, and that's not always, uh, the most valuable or differentiating position to be in.

Um, what do you see working in terms of adding real value for customers with ai?

Satyam: See, I think first of all, uh, any data analysis, uh, at least, uh, language data analysis, uh, should be, uh, straightforward. Uh, like just I, I'll give you an example. A teacher normally reading an essay, uh, is coded by a teacher. Uh, Assuming they have 30 students in a class, uh, and they are, uh, reviewing the 30 essays given in know 10 minutes each, that's a [00:05:00] sizable investment with AI models.

You can immediately kind of review it, kind of define and, and kind of cluster them into mistakes that have been made and that feedback can be given even before a teacher reviews it. So that's what I mean by democratizing a lot of these technologies in a way. Like, Hey, how can we save time? I'll give you an example for user research.

We get data in, in, uh, a lot of ways, uh, again, uh, as, uh, content and con as words and immediately pattern recognition. And that's, that's as simple as defining frequency of words that are used. If everyone's saying frustrated, frustrated, frustrated, that means the theme is frustrated. If everyone's saying, this is awesome, awesome, awesome.

The theme is awesome, and I think these are things that you don't have to. Someone to physically read through it. And this is where efficiency can be built at a very high level. Uh, you are looking at i V R systems, uh, people are calling and having conversations with, uh, of a customer service. Uh, you start looking at, you know, what they're saying and now there's a intelligence behind that's kind of starting to understand the tone of the conversation.

[00:06:00] If someone is like, you know, Talking to the, even the I V R as, as much as everyone knows, it's, it's, uh, it's not a human. You still can build an experience around it. And that's where the power of, you know, at least on the basic level, I would say the low hanging fruit, uh, itself goes. Uh, we were talking the other day, uh, with a company, uh, where they were building a model around pricing, uh, you know, cars in a dealership.

Uh, and, and the simplest thing and, uh, automotive and dealership is, uh, knowing all the data that's available around you, which is like, how many new cars? How am I gonna get in the inventory in the next lot? You know, how much did the last person pay? What kind of incentives are going on? All these can, the system can automatically think through it and then kind of come back with some recommendation.

So again, it doesn't need to make decisions for you like a artificial intelligence, but it can at least give you. A decision support system to kind of say, Hey, this is where it is. The same goes with radiology, everything. So again, you start seeing these examples where AI is now a assistant. In, in true terms and kind [00:07:00] of giving you an opinion, like again, as I said, a teacher could get an opinion on which essay needs more, no need help, and maybe she can spend all that time coaching that person, then actually reviewing that work.

Mm-hmm.

Aransas: Mm-hmm. Yeah. It seems like what we're really doing is exactly what we did 10 years ago when we said what should be an app and what should be a human provided support and. It should be an app, if an app can do the job better than a human, and it should be a human, if the human can do the job better.

Dave: Absolutely. Yeah. Um, it makes me wonder, and, and maybe you can help us with this, uh, what do you see as the role being between humans and ai? Um, what a lot of people are talking about that right now and, uh, I'd be interested in your point of view.

Satyam: So I'll go back. I think that's a very, very good question, Dave.

Uh, I'll go back to the line [00:08:00] of work that we have been all involved in, and this is, we are in the world of human centered design or user centered design. Uh, And, and the basic premise of that is you need to know your user. You need to know the experience, you need to design that, and then kind of translate that into, so in, how do I take technology to make that experience happen?

Uh, and I, I would say the same applies here for AI to, I mean, humans interact with AI to solve a problem at hand. Humans don't interact with AI just because they can and they should. Uh, the same goes to apps, same goes to web, same goes to meta. Anything. The, the, is it solving a problem that is fr you know, painful for me?

And again, you don't wanna white toman, you want a painkiller here. Uh, so the same applies here. And I think, what's the interface? I think the interface is, Whatever, and wherever it needs to be. I mean, good experiences and good technology is giving me what I need when I need it in the context, uh, and uh, in a way that I can immediately be actionable with it.[00:09:00]

Uh, and uh, and that's kind of where I think if AI has a lot of the, and if you look at that, Framing AI has a lot of places that it can kind of get involved in. Uh, you know, friend of FO identification, AI has already been involved in that for a long time. And military applications, right? In the same cases, uh, you know, you have security cameras nowadays with, uh, you know, a lot of.

Uh, and, and you can actually start defining AI in there. Now, the interface between human and ai, uh, I would say initially should be, uh, an assistant basis, uh, with some level of autonomy that you give it. Uh, for example, uh, you know, you today have security alarms where an alarm goes off and have no intervention are done, is, is done.

Then you effectively. We'll call police. And that is why I would say the first level of autonomy where AI can make a decision on your behalf and let's keep it there. Within that range of experiences, there's a lot of value to be created. Uh, and I don't think we need to even think about, you know, the, the.

Construct of it's gonna self-drive and self [00:10:00] fly and self decision make or self attack. Uh, in, in the case of military, that is all I would say, you know, years and years away from it. As much as, you know, prototypically, it can be done, but the same thing as, you know, you, it's not prototypes. And then how it works is very different.

Uh, and we all realize that also in our line of work, but I would say within the first couple of low hanging areas, there's just billions of dollars of value creation.

Dave: So, so in, in many ways Aransas, it is kind of like what you were describing, which is that, uh, it's there to assist the human. At least in the initial stages, right?

It's there. Whether it's you're creating some kind of, um, image that you couldn't create or you couldn't create in the same amount of time, or you are trying to, um, analyze a lot of text or you're trying to, to [00:11:00] automate certain processes. It really helps to advance those types of activities, at least in the near run.

And you're right, there's a lot that can be done immediately, uh, for companies and I think that's why they're so excited about it. And also nervous,

Aransas: I think too, about the role of employee experience here as well. And so many of the examples that you gave Saam, you can see the human on the other side, who is.

Ideally freed to do more strategic thinking, to make decisions with greater insight, more efficiently, and hopefully feel a greater sense of purpose and impact in their work as a result. And I don't think that's being talked about enough in this moment about the, the potential to solve some of our employee engagement crisis at the same time by using ai.[00:12:00]

Satyam: Absolutely. Absolutely. And again, a, a good teacher as I, I'll just to go back to that model, uh, my kid started school this week. Uh, you know, a good teacher has only so many hours to give. Uh, now are they going to, and you, you see this consistently play out and, and public education, or in fact any education context that, you know, a good teacher is spending more time outside school, uh, for being a better teacher, uh, because they are evaluating it, they're prepping, et cetera.

Just within that, if you just did a, you know, simple analysis of the day that, and the processes they work on, there's just so many ways that you can ma enrich their lives and make their lives even more effective. Uh, doesn't mean that the teacher goes away. I mean, yes, absolutely. Uh, but the fact is a student can go home and say, Hey, I wanna learn this concept.

Can, you know, and Khan Academy has done, uh, some good stuff now with ai. Uh, you know, you effectively can say, Hey, can I learn the concept? And that's again, from a student standpoint. You know, I, I only had one way of learning it, which was my teacher and I, [00:13:00] and that was the interface. And, and with the advent of YouTube, now I have so many people who can teach me that.

Now with ai, it actually can teach me the way I learn. And that's because it kind of, it. Based on the cues that I have and how I've been asking questions to it, uh, or what I've learned prior to that, it can actually start defining, Hey, Sathi, remember that story? And then now kind of connect that so it can actually build that level of modality.

And together, when you start looking at like the teacher's better life, the student has a better life, uh, and everything kind of, kind of cascades really well, but effectively as you called out, uh, You know, AI can make a lot of people's life easier if you know how to leverage it. And I think, again, like anything, I read this story a long time back that one of the presidents, uh, of America did not even want to get electricity in White House because he was scared that he would get electrocuted.

That's kind of, again, if you approach technology with that way and possibly, you know, wiring may not be as, as, uh, as, as, uh, as established as it is today. But again, we had to approach it with, uh, you know, [00:14:00] Positive optimism, uh, and obviously I would say cautious optimism and, uh mm-hmm. And there is a lot to gain outta it.

Mm-hmm. I

Dave: love that. Mm-hmm.

Aransas: I love that. And there will be, there will be big winners here and there will be companies that, that, I mean, I, I'm thinking about all the, the, the, every subscription I have in my life right now, every tech subscription I have has introduced an AI feature in the last year. Some of them.

Are life changing and they add tremendous value to these subscriptions because they allow me to get so much more out of the investment that I'm making, that I'm literally in every one of these cases that I can think of just waiting for them to raise their subscription prices dramatically because the value has now so outpaced the cost.

Satyam: That is so true. Uh, if I can add one very powerful, uh, uh, observation and insight that I'm actually seeing in the market. [00:15:00] I say there are two kinds of companies, uh, and this is what, this is continuously playing out for every technology that has come out, there's companies that are ready for it and companies that are adopting it, right?

And they're just two different, and organizations that are ready for it, they actually, that they know what, who the users are, they know the pain points. They've been just waiting for a technology to solve that pain. Point. Mm-hmm. So as soon as the technology comes, they have the use cases, they know where the scenarios are, and they immediately, their team, everybody is very much in tune with like, let's go for it.

Now we have something. So they're constantly looking for a solution and now a powerful technology comes. Now you're just going to go and do it. And then there are companies out there who are like, Hey, someone else is doing it, so I need to do it. And they are building a app like in the old school or a mobile, uh, product or whatever, because someone else is doing it, right?

Mm-hmm. And, and that is, and I, I would say unfortunately, majority are the ones that are the ones that are following. Uh, and uh, and that's why I say that, you know, you cannot win in this game [00:16:00] of user-centered design and as, as a strategy. Uh, if you are following, uh, your com, your competition or you're following someone else, you only will win.

If you're following your user and you understand the pain that that school district, you know, uh, that actually understood the pain of that teacher will be so much faster and kind of leveraging it, because unfortunately, they are a hundred things that you can do with ai, but which 10 do you want to do is actually the mark of a more effective leader in organization.

Hmm. So let's

Aransas: talk about that, and I know it's what you are most passionate about. How, how do you advise companies to begin the process of understanding the most important customer needs?

Satyam: See, I think a lot of times our conversation and this as. And I say this as, you know, as fellow practitioners here, right?

So what we think is, is stat is modest operandi and just sta status quo, uh, is not the case for majority of the [00:17:00] folks. It still is a great unveiling for a lot of the leaders out there. Uh, so a lot of the conversations on me talking to leaders is still as, as, as sad as the situation is, is like, who's your user?

Can you tell me your top users? Can you tell me their top problems? Can you tell me what you're doing about it and tell me how you're measuring that you're doing something about it. And right there, the conversation stalls. Uh, and the reason it stalls is because many people have opinions. They still don't have scientific.

Mm-hmm. You know, uh, data that's coming together in a con. And again, that means they don't know the journey. They don't know what it is. They've spoken to a customer one day here or one day there. And that effectively is what they're giving as anecdotal data, but it is not pattern data. They're not able to clearly state that, you know, hey, Eight out of my users have this pain 0.4 of these users get stuck at this particular friction point in the journey, and they're not able to articulate that.

And therefore I'm doing this and, and I'm trying to evolve there. Uh, and, and that's where you start seeing that they are not even user-centered by any [00:18:00] means. Uh, so that's the first part. And then you get a very small subset of them who are like, Hey, I, I know the pain point I wanted. Again, let's take that again.

The example of the teacher. I know that I have teachers who are spending way more time and they're probably spending less in, in, in test set up, test evaluation, et cetera, than actually, uh, Uh, you know, coaching and, and, and, uh, you know, supporting, uh, you know, students on, on their side. Now that's a problem that they live with and they're thinking about, and that's kind of a friction point that they think about.

Like, Hey, amount of time they spend in assessments is kind of longer than they spend in teaching. I'm just giving an example. Mm-hmm. And that is effectively what, uh, uh, you know, they need to focus on. So again, leaders who are much they, and then they're work ready to work with people. And then kind of go through that again, our line of.

Profession, uh, is not unfortunately, has the word design. So many people kind of immediately think it is design as a visceral pro profession. But actually they, when they, uh, and then I write it in the book too, [00:19:00] is the big D design is missing in the, in the pursuit of the small d design of the craft. Uh mm-hmm.

And, uh, many organizations still forget, like, you know, as I said, you know, systemic things of who my user is, what the pain points are, what experiments am I running around it. How am I evaluating it? So that's basically what I would say is, uh, what's, uh, the conversations I have with leaders and, uh, and the ones that are able to kind of leverage the power they see, you know, in case millions and billions of dollars of value creation.

Dave: So good. And, and we are to, we totally agree with everything that you're, you're describing the customer is still going to be the center of everything that is going on, and we really need to understand what the customer is all, all about going forward. Absolutely. One of the things that we've seen though, as is that as.

Uh, technology has done more and more for us and as [00:20:00] solutions have become smarter and smarter because that trajectory of getting smarter, um, started well before artificial intelligence, that people's expectations change and they evolve. And I wonder, as you think about the future of user design, How will we have to think about design in the future so that we can meet their expectations?

Because to simply say that, um, the, the customer is the center is probably not enough. We need to probably change the way we think about customer expectations and, and the solutions that we're creating in the future. What, what are your thoughts there?

Satyam: So, uh, I'll answer your question in a, in a little, uh, different manner, but I, I kind of get the gist of your question for sure.[00:21:00]

Our line of work is that we are trying to understand the pain of a user and then trying to convert on a delight, right? That's basically, I would say the simplest format of what are we, we in the pursuit of, you know, converting pain into delight. So that means I need to understand what pain looks like.

And then I, I need to understand how delight, delight is. Like, you know, the say the day you actually, you know, first saw auto complete in one of your, you know, word processing software, and you're like, oh, I just hit tab and it automatically completes the rest of the sentence. That's delight. But the unfortunate sense of most users and as a, as a, as.

As humankind overall is today's delight is tomorrow's expectation. Mm-hmm. Today, if you give a smartphone without any touch, uh, capability, everyone's gonna say, what the heck is this? Right? And, but the first time a, a touch screen, a smartphone came. Effectively, it was the biggest thing. The same applies for if a word process today does not have auto complete.

They would assume that this is not even in, uh, [00:22:00] basic. And that's the unfortunate power of, you know, we start getting used to the, uh, you know, uh, delight. That was yesterday's delight as today's expectation. So we always have to be constantly seeking out all the pains that we have and trying to find better ways of solving it and so on.

The good part is, If, if you have a good system in place, uh, where you are understanding a user, following a user, empathizing a user, you start seeing problems and pains and friction everywhere. And that just is the nature of our line of work that you know, you know, you can see in end number of ways that why is someone something done a little different.

Process engineering design. Workflows, all those things can be adjusted. Uh, and uh, and that's why I would say the expectation, uh, for them is that, you know, someone is thinking about it. And, and I always say that any great experience you've always had in your life, whether that's, you know, offline, online, whatever that is, someone has designed that.

And they've designed it, it just doesn't magically serendipitously happen. Uh, [00:23:00] it it just because someone has thought through, you know, that whole process, uh, and, uh, and, and, but they, they think through it by understanding what is your, as, as what the, the, the user's pain and so on, so forth. And you had to constantly be in the pursuit of that.

You can't just be, again, one of the things. As a separate level, I learned in businesses that businesses are not one act companies and the best businesses are act one, act two, act three, and they're to keep inventing, keep evolving themself. Uh, and and the same applies to, you know, uh, our understanding of pain and delight.

Delight, delight because, uh, one time Netflix was the, Best streaming company out there today. They are paying more and now Netflix has to compete for it. Uh, same thing. It's just not on delight. So it needs to keep pursuing, uh, the user and the pains that they have. Um, and I'm just giving an example, streaming companies, I mean, Disney just kind of, I think beat a Netflix in terms of the total number of subscribers, and that's the nature of it.

Now, that's standard, that's everybody's doing that technology stack has improved. [00:24:00] Uh, but expectations are, are only getting. Better and better. Uh, the other thing I will also quickly share, uh, is today's world is becoming usage based. So we weren't from a, Hey, I'm buying a software to subscription to now usage based.

And all the more that's, I said, expectation is if I doesn't work for me, why should I pay? Very different mindset from like, I bought the software, it doesn't work for me. Okay. I just kind of put it in the shelf.

Dave: Yeah. Oh, absolutely. Yeah. We just had an episode, um, where we talked about subscription strategies, and that's exactly, uh, the new world that, uh, or that's the current world that we live in, is that people expect to be able to use immediately.

They need, they expect to be able to, oftentimes for free, do some pretty amazing, um, things. Uh, so true.

Aransas: Yeah, and I think it'll be interesting to see what chapter three is for Netflix [00:25:00] because they, they did, they were using predictive analytics to really differentiate early on, and they changed the course of streaming history with their work.

But you're right, they have not introduced chapter three yet, and it will have something to do with ai no doubt. Mm-hmm.

Satyam: Absolutely. Absolutely.

Dave: You, you know, one of the things that you make me, um, think about is a concept that we've talked about on the program before, this idea of superpowers. It used to be that, um, you know, superpowers were kind of for the movies, but if you look at what's, I mean, I was just reading the Wall Street Journal, uh, the other day and there were three headlines that had superpowers in them.

Uh, people are starting to expect. That they will be able to do things [00:26:00] that go far beyond, uh, just normal abilities to accomplish things in an efficient and effective way. They are, their expectations are changing dramatically around the job. That the customer is trying to, to get done. And, and that job is not necessarily like one job, but it's like six or seven or 10 and it, there, there needs to be a little bit of magic associated with it.

And I think that that is all good. I think that that's where things should be headed. What are your thoughts, Satyam?

Satyam: Uh, uh. I'll log in, try to reframe this a little bit differently, right. So I come from a military family and there's a construct in the military, uh, called force multipliers. Uh, and force multipliers are assets in the, in, in the field that actually multiply any other assets that you have.

Uh, it could be like, Hey, I have a early warning system in the, in, uh, [00:27:00] in the air that effectively tells me, you know, what else is happening in, in the air. And effectively that allows me to. Deploy my assets more strategically. So that's kind of what forced multiplying as a Strat, as a a strategy, at least a military is.

And I think technology for us is forced multiplying if we use it right. Uh, there's a time, and again, you see this everywhere today. Uh, you know, whether a doctor looks at, and I'll give you a, a, a good and a bad example. A bad example is people like saying, Hey, I put the data in chat, G p t, it gave me this data, and therefore I put this out as a result.

That is a lazy way of doing it. Uh, however, if you are in a meeting and you say, Hey, what, is there any other, another idea that I could actually think about? Or is there something I've not thought about? Let me go to chat g p d and say, Hey, can you gimme 10 ideas on how to kind of do a good holiday party?

I'm just giving an example. Uh, and I suddenly may find one, one more idea there that I may not have thought through. Uh, or I may actually even further fin, uh, [00:28:00] you know, uh, fine tune it by saying, you know, Hey, I want a budget of less than, you know, a few hundred dollars. And again, maybe a few more ideas that I thought through, but all that happened in five minutes.

So it force multiplied my decision making and kind of allowed me to kind of free up that time. And I think that is the power as, as you kind of start looking through that. And the superpower of, as we talk about the superpower, is that I can move faster. But then we, we look at superpower as something that is un inhuman or, or like unhuman or is something that is actually, uh, you know, very hard to achieve.

But forced multiplication is actually much more simpler strategy. You know, I'm looking at a hundred resumes. I'll give you an example. I. Uh, all I need to do is, you know, if AI could kind of build a model around, you know, how we talk and how we, uh, just internally and in our language and then kind of just look at some resumes and say how other people kind of, what are some of the similar ways of tones and it could kind of fine tune in, gimme even five resume resumes that are in that tune.

Doesn't mean I'll reject the remaining, but at least I'll start with the first [00:29:00] five. Force multiplies my process of saying, which five do I talk to? Which five are this are similar to the. Cohort I already have, and that's kind of where I, you start seeing efficiency kind of written all over and more importantly, that's a superpower for me if I can quickly click a button and get five different resume resumes to look out of a hundred that I have applied.

I love it. Yeah.

Aransas: Never thought about it that way. Again, it goes back to this idea of bringing the human and technology together to create greater power through leveraged strengths of each. So rather than expecting, which I think is a lot of the fear piece of ai, right? Is that this fear that AI will become human or take over human roles.

But I think really the conversation is what are the strengths of either and how do you multiply those strengths in partnership?

Satyam: I. Absolutely. Absolutely. Uh, good.

Aransas: So a lot of the examples that we've been talking about are really, I think, centered around. Companies that [00:30:00] see themselves as digital experience companies already, they are tech led, they were born of technology.

But what about for these companies that are more legacy brands that have not been, um, have not had a strong, a digital experience over the years? How do you see them entering this space meaningfully?

Satyam: See again, I Tech is a way to solve a user's problem. Uh, and, and that's, let's say a tech is, uh, tech by itself doesn't mean anything, right?

And agree. And that's why we have so much hardware, consumers, uh, you know, products that we actually sitting at home. I have like three Alexa, you know, mission system setting, that, that, that's tech. If it doesn't make meaningful change to my life, it, it's just gonna sit there. And I have so much, in fact, uh, I was just, I'm piling up my, you know, electronic, uh, you know, donation pile.

Uh, however, [00:31:00] On the flip side, as you said, legacy companies, legacy companies are companies that have been there in old school companies that have been doing what they're doing. They, they understand. The product that they're building Now, again, I'll give you an example. Maybe it's in, uh, you know, uh, industrial manufacturing.

Uh, a lot of these things actually are, uh, are human centered to the point that there's a skill person that's reviewing and, and looking at things. But when you start thinking about like, Hey, is there a way to add to their aspect? And now, because even, uh, succession planning is hard. Uh, in, in any skill-based profession.

So you kind of start looking into like, Hey, how can I, uh, add to that process? I'm just giving you a very basic example, but the, the most, uh, biggest essence is you need to know all the users in your system. And, and this doesn't only mean the person in the factory floor. This means your, uh, you know, janitor.

This means your security. This means your. Uh, you know, your suppliers, you effectively start understanding [00:32:00] everyone that in your eco ecosystem that, uh, you touch, and every user that actually is left with the impression of touching your system is effectively a user in this system. And then you think about how can I make it more efficient?

Uh, it could be a vendor who's trying to figure out what the demand forecasting is. It could be, uh, and again, all I'm saying is just make a list of all the pains. Nothing else. Don't even think of technology. So, which is a stack, understand your user. Understand their experience, then design it and then go figure out is there a technology to do it or is there a process engineering to do?

It doesn't matter. But I think, and most times you will notice that, at least in today's world, that there is a technology element to that design that you ended up doing and, and mm-hmm then you actually will start seeing that. How do you do that? Many companies are not built that way. Uh, again, uh, This goes back to organization design another, you know, favorite topic of me in the, in the, in the structure of b, uh, in the big D.

Organization design. Today, most tech companies [00:33:00] have somebody called a CIO. A CIO is the tech person for most of these legacy companies, and that's the first thing that many companies do. Uh, however, the c i O is in charge of all IT tools and systems. Uh, What actually happened during the pandemic was very interesting because as soon as the pandemic happened and the lockdowns were starting to kind of come together, the CIOs became from chief, uh, information officer to chief Innovation Officer because suddenly they had to figure out how would people kind of work remotely, engage, remotely, collaborate, change the way they are enga, uh, working through things.

And now I had to think of technologies to kind of make me as, as multipliers not as, Uh, and now you're trying to introduce Zoom in the process, uh, and uh, or whatever, like you're starting to think that instead of saying, Hey, I'm just a IT person, that I just make sure that everything is, those computers are working, and so on, so forth.

I mean, that's unfortunately how that most CIOs are perceived. That they run, they keep the systems running, but now you're looking at systems are the company, if the [00:34:00] systems don't happen, the company kind of dies immediately. I think that's the shift that's happened over the last couple of years, and now they are noticing that there's a lot more value.

But again, if you know your users, you know their pain points. And you know how you can, what, what kind of solutions would solve it and if technology is the way to kind of go for it, which I see a lot of companies have done much better in. Uh, and I'll be surprised that, uh, if, if companies that don't, can't see technology as a multiplier there and then go ahead and kind of, uh, leverage it, that's where I would say is the path to do that.

Most, most legacy companies are still stuck with, uh, uh, the old way of thinking and thinking that, you know, what got us here will also get us there. Mm-hmm.

Aransas: Mm-hmm. That's right. And because, and it makes sense, right? From a psychological perspective, because that's what worked. So they have evidence of that.

And so they're going to turn again, again, to that in large part because that's what their shareholders believe is effective. So [00:35:00] it, it sounds like so much of what you're coming back to is. A, a confidence in understanding these user needs. And yet, I think you're also saying that the biggest challenge to most of these companies is they don't have confidence that they really understand their user needs.

They've gotten them piecemeal, they have different data sets that are competing. So how do they find that level of confidence that they're meeting the right needs?

Satyam: I, I'll start with an analogy, uh, and that would hopefully explain the, the perspective on this. Uh, no military or professional military in the world, uh, that is worth their, uh, you know, uh, their respect, uh, goes into any mission without intelligence, I.

Uh, and Intel is a big part of that. Just any process, uh, of any standard military operations. And I think the same applies for businesses, [00:36:00] uh, and Intel is a profession. Intel is a, is multi-year process. Uh, and, uh, you, that's kind of what it takes. However, uh, it starts with, you know, small steps and it starts.

Playing out, giving dividends, uh, you know, fairly soon. But it's a process that you have to keep getting better in the system that you build. So, again, the same way most companies need to have, uh, an a clear, uh, you know, a team or a group of people that are looking at who the different users are, what their pain points are, what they are doing about it.

Uh, I'll give you examples where this doesn't manifest, and including in many of the large companies here in the valley. Uh, you know, sales is doing their thing, marketing is doing their thing. Uh, customer service is doing their own thing. Product is doing its own thing. And, uh, the only person that they all report to is a C E O.

So, uh, I, I have jokingly say that the c e o is the chief Experience Officer. It's not the chief Executive Officer, because that, that's the only place where all those experiences kind of finally report into one place. Uh, and so when a [00:37:00] customer calls in, in a, in a, uh, organization that's not affected and everyone's running their own silo, Uh, a customer calls and they get a different experience with customer service, with different, with professional services, different with the product.

And, and that's kind of how you're, you're shipping your a structure to, to outside and then getting them to kind of think through that. Uh, so therefore, the first thing is you need to have a. Uh, a very strong research team that's kind of extracting that data. You need to have a strong culture around the organization that takes that data and then kind of institutionalizes it.

Uh, today's world you have, uh, you know, uh, in a highly distributed place. I. Where we all live in and we work, uh, it's uh, you have engineers sitting, uh, half around the world who have no idea what the context of the user that is probably sitting in another half around the world. So, and this is where the inefficiencies creep in, because if you don't know your user as well, I.

That developer, all that they're gonna do is just take the spec that's given to them and code it. And they've obviously, you know, they will not be able to, if the empathy is not, [00:38:00] or the shared empathy is not built in. And I'll give you an example. Like you can be building for insurance companies in the US but sitting in, you know, let's say Ukraine or in India, but, uh, but that's not the same insurance system.

It's a different thing. So again, you have no empathy for the because, just because you don't have sensitization. And therefore I will code whatever is given to me and I'll code it. And then, uh, effectively, you know, A good chance that it'll not work that way because somebody in between, there's a, there's so many points of failure there.

So this is where I say the system isn't built right. You first, you don't have the data to kind of stimulate it. Uh, then you don't have the right people to kind of act on it. Then you don't have the right governance to kind of, uh, track it and review it. And, and therefore, you know, many companies, even though it's a great, uh, concept, uh, as simple as it sounds, it, it just ties because it just doesn't get, uh, the right nurturing.

Aransas: Yeah, I think empathy and context to me are the big words in that, and it's certainly what we see. It is a lack of understanding of the human who [00:39:00] exists beyond their interaction with your product. I. And by, by assuming that all of the decisions are being made in the click of the button, you're, you're losing the entire picture.

It's like you've seen the, that, those videos where they, they show you a little picture and then you see it start to zoom out and it can, right. And it starts with like, A boy reading a book and you realize that the boy is reading the book in the middle of a globe, which is in the middle of a forest, which is in the middle of the universe eventually.

And, and I think that's a lot of what happens is we, we lose the context and are we gonna see everything? No. But we need to understand the important things that are influencing our customers. Absolutely. Such a great conversation, Satyam. I. I, I feel like there are already so many really actionable points in this, but if you were to give one [00:40:00] piece of advice to experienced strategy companies today, what would you say?

Satyam: I think the first thing I would say is that we have a lot of, uh, good work to do ahead of us. Uh, but to do that, I think we need to approach it from a perspective of, uh, and I say, I call it, at least in my firm, I say, if you're not generating 10 x the value for every dollar invested in it. That means we don't, we, we don't really understand the problem well, or we don't understand our profession well.

Uh, so I think that's kind of, I would say as a clear call to action. Uh, I've seen opportunities where, you know, as I said, we have multipliers and this whole profession is a force multiplier. As a matter of fact. It's not a, it's not a vitamin. It's solves a lot of pain for a lot of people. Uh, and it solves it in a way that is highly efficient when it's done right.

So, again, I say, you know, this is a force multiplying a line of work, sport, multiplying, uh, force multiplying a strategy. Uh, and we just have to, uh, you know, uh, keep up to it and then drive it. And again, it's very [00:41:00] easy to kind of deliver the design, the ui. And this is also for a lot of the business leaders, because that's the easiest and the visceral thing to do.

But more importantly, I think there's, there's just so much intrinsically and systemic things that can be adjusted, uh, by when you are really e user centered. And I think it's a powerful strategy and everyone should be doing it. And we have a lot of work ahead of us as new technologies continue to come together.

Aransas: I love it. Yeah, and and I think you're saying be a painkiller, not a vitamin, but I actually think maybe let's be painkiller and a vitamin. Right. Let's kill the pain and enrich. Right. Which is really where we're getting to, which is adding that extra nourishment through those superpowers and the force multiply.

Yeah. Great stuff. Thank you so much, Satyam. Thank you, Dave. For those of you listening, uh, find out more about Satyam and his work in the show notes and over at experiencestrategypodcast.com. And, uh, then come and join us for the Digital Experience Collaborative, which is [00:42:00] launching as a part of our Experience Strategy Collaboratives program kicking off in September with, uh, rolling enrollment throughout the year.

So, uh, check us out d m s to learn more. Thank you

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