Podcast | 10 Jul, 2023

Episode 4: The State of the Generative AI Market: Parallels to Speech Recognition and Enterprise Software

Alex Olesen


Alex Olesen

VP Vertical Strategy & Product Marketing

Vipul Vyas


Vipul Vyas

Senior Vice President of Go To Market Strategy

Vipul Vyas, Senior Vice President of Go To Market Strategy at Persado, chats with our podcast host Alex Olesen to share how AI, particularly Generative AI, drives efficiency and effectiveness in business. Vipul is also an adjunct faculty member at the University of San Francisco, focusing on emerging technologies and also on the healthcare space. Generative AI solutions such as ChatGPT, for example, are effective. Even small teams can create a lot of content in a short amount of time using these tools. It’s an efficiency gain similar to trading in a push lawnmower for a riding lawnmower. But, businesses also think in terms of performance, not just efficiency. 

“Yes, you can write it faster now, certainly. But, did it get more eyeballs? Did it get more traffic to whatever potential product you were trying to sell or promote? That is where efficacy plays a role,” said Vipul. 

Performance or effectiveness is what separates Persado from other Generative AI tools geared toward enterprise marketing teams. Marketers need effective language that generates an incremental amount of conversions. Persado Generative AI is a solution for the issue of conversions among digital marketers. 

According to Vipul, there are too many great ideas stuck in people’s heads. Vipul ultimately sees Generative AI unleashing a lot of creativity. Now people who can’t write or draw as well as they would like to can express their ideas in new ways.

Episode Transcript:

00:00:00.000 –> 00:00:00.880
Yes, yeah.

00:00:01.360 –> 00:00:05.839
[email protected]: we’re we’re recording. All right. I’ll start in a couple of seconds. Can you hear me? Fine.

00:00:06.240 –> 00:00:08.160
[email protected]: Yeah, perfect.

00:00:11.410 –> 00:00:30.809
[email protected]: Welcome back to motivation. AI matters I’m really excited for our guest today. I’m joined by Prisato’s senior vice president of go-to-market strategy. Vippel v us vit bull it’s great to have you on on today’s episode.

00:00:31.020 –> 00:00:42.000
[email protected]: Now I know you and I work really closely together. But to start things off for the listeners. Do you mind giving us a bit of background on who you are.

00:00:42.090 –> 00:00:45.650
[email protected]: your career? And what led you to Pristado?

00:00:46.670 –> 00:01:08.630
Vipul Vyas: Sure I at Prisado currently I, as you mentioned to head up our go to market strategy team that encompasses a few things. That’s an unusual title and role. But it involves our sales. Engineers, solutions, consultants that are synonymous in our organization. along with their product marketing organization or value engineering group, which really quantifies and helps us understand the business impact

00:01:08.630 –> 00:01:19.639
Vipul Vyas: we have on our clients along with our education enablement team and our commercialization team. So it’s a few different groups that also under one umbrella

00:01:19.640 –> 00:01:34.260
Vipul Vyas: that we use to really provide our customers with. a high touch experience in terms of understanding and quantifying the impact that we have giving the tools that need to be able to succeed on their own

00:01:34.270 –> 00:01:40.570
Vipul Vyas: etc., and then just helping them understand their solutions as well. in terms of career.

00:01:40.660 –> 00:01:57.310
Vipul Vyas: I can go all the way back. I was a management consultant for many years until I got my first real job. so I was at price Waterhouse and booz Allen did my mba at Dartmouth undergraduate Virginia, and my first quote, unquote, real job is after I left

00:01:57.390 –> 00:02:26.519
Vipul Vyas: booz, Allen. I went to a startup. had to actually be convinced to go, because that was too risky, and this was in 1,999. It was in the speech recognition, space, went from speech, recognition to voice by metrics. so me see, machines understanding what people say to machines, understanding who said it? And then I would. Did a little bit of a time and health care as another start up that I I founded, and then I found myself at Prisato back, helping

00:02:26.570 –> 00:02:48.620
Vipul Vyas: make the robots smarter, as I was doing with speech, recognition, and voice by metrics. So that’s how I found myself here at Prisado. I’m getting back into the natural language processing world that it started out in almost 20 years ago. I also am an adjunct faculty member at the University of San Francisco, focusing on emerging technologies and also in healthcare space.

00:02:49.160 –> 00:03:09.849
[email protected]: So you sit in a very unique position at Prisato, one which I have a I’m fortunate enough to have a front row seat to participate in. We’ve had some great guests in the last couple of weeks talking about what generative AI means to marketers. We had a guest on last episode to talk about

00:03:10.050 –> 00:03:14.540
[email protected]: security and safety around implementing these technologies.

00:03:14.790 –> 00:03:35.629
[email protected]: Your focus in our day to day is around the business implications of using generative AI. Talk to me a little bit about the efficiency and efficacy framework that you’ve been working on the past couple of months, because I know it’s gotten a lot of attention from executives through Prisado.

00:03:36.390 –> 00:03:48.249
Vipul Vyas: Sure. we look at it. As you just mentioned in terms of AI specifically generative AI can have an impact in terms of improving efficiency and also impacting efficacy.

00:03:48.380 –> 00:04:00.779
Vipul Vyas: And so what that means is, it can. Obviously, you know, people have visually felt things like chat. Gbt, if folks have used it, make doing a specific task much faster.

00:04:00.800 –> 00:04:10.750
Vipul Vyas: You know what would have a report that would have taken, or a blog post that would have taken, you know, a couple of to 3 h to create happens now in minutes, and then can be edited and smoothed out

00:04:10.770 –> 00:04:20.820
Vipul Vyas: in no time at all. So there’s a massive efficiency gain, you know. It’s like going from a push real lawnmower to a riding lawn. Or

00:04:20.870 –> 00:04:39.180
Vipul Vyas: there’s this. Obviously, you know a big difference. Or you know, men with shovels or people shovels to a back home. Right? It’s just that kind of just brute power brought into one person’s hands. And that’s pretty substantial. Now, in terms of impact efficacy.

00:04:39.190 –> 00:04:48.160
Vipul Vyas: that really, we think of in terms of performance. So is what is being produced. also getting you a

00:04:48.290 –> 00:04:51.739
Vipul Vyas: increase in the business metric. It’s trying to move

00:04:51.910 –> 00:05:09.910
Vipul Vyas: And so what I mean by that is the blog post that I alluded to before. Yes, you could write it faster now, certainly. But did it get more eyeballs that it get more traffic to it, get more through traffic to whatever potentially product you were trying to sell or promote. That is where efficacy plays a role.

00:05:10.280 –> 00:05:17.249
Vipul Vyas: And in most cases these technologies, generally speaking, have not started impact

00:05:17.310 –> 00:05:21.059
Vipul Vyas: that space. They do so in a tangential way in that.

00:05:21.280 –> 00:05:42.800
Vipul Vyas: Now you’re able to do more things and get to more things that before we’re never gotten to just because you couldn’t. And so, having lifted the constraint of limited labor, you now are able to do more things, and you’re becoming effective through that mechanism. But at a discrete sort of unit level. most of the tools are still on

00:05:43.020 –> 00:05:47.080
Vipul Vyas: the trajectory of improving efficiency.

00:05:47.180 –> 00:05:52.700
Vipul Vyas: Now. when you do both, when you can improve both efficiency and efficacy.

00:05:53.130 –> 00:05:55.270
Vipul Vyas: That’s when things sort of get

00:05:55.380 –> 00:05:59.209
Vipul Vyas: warped or you enter it sort of a a totally different world.

00:05:59.240 –> 00:06:14.829
Vipul Vyas: And you can do many more magical things, the the constraints that you were living within the past of completely disappeared. And so things can have fundamental shifts and paradigms. Can really, you know, can move. So

00:06:15.240 –> 00:06:24.880
Vipul Vyas: that’s where I think we’re heading very soon is where we’re going beyond just efficiency gains and also impacting efficacy. And the combination of

00:06:24.930 –> 00:06:38.470
Vipul Vyas: those 2, which is the ultimate, better, faster, cheaper. is where there’s going to be even more profound impact. So I can give you some examples that very quickly. you know, in the legal space.

00:06:38.580 –> 00:06:44.040
Vipul Vyas: you were, you’re now able to efficiently review

00:06:44.200 –> 00:07:13.090
Vipul Vyas: dozens and dozens, if not thousands, of contracts, and look for compliance to a new indemnification or limitation of liability framework like how many of these are in line with our new standard? How many of these are not, and before you need, like a paralegal, or maybe in a lawyer, to wade through all of these, and that is arduous. Mistakes can happen. And now you can just do that much more efficiently. but you can also get some interesting efficacy gains where

00:07:13.280 –> 00:07:20.109
Vipul Vyas: that are related efficiency where now we have inbound complaints for consumer

00:07:20.430 –> 00:07:26.500
Vipul Vyas: inbound messages and communications that may flag for litigation, litigation risk.

00:07:26.610 –> 00:07:29.139
and you just couldn’t ever get to them all.

00:07:29.300 –> 00:07:32.959
Vipul Vyas: And so you couldn’t flag which things were potentially sources of

00:07:33.090 –> 00:07:47.760
Vipul Vyas: legal exposure and which ones were not. Now you can actually sift through those and say, okay. The person who slipped on a great better grocery section probably is more of a concern than someone who’s describing it about our prices.

00:07:47.850 –> 00:07:52.629
Vipul Vyas: and so you can wade through that and identify things that before.

00:07:52.980 –> 00:08:07.220
Vipul Vyas: just because of lack of manpower, lack of just resources, you weren’t able to get to one on the efficiency set, and then also being able to pattern, recognize and discern which things represent a risk

00:08:07.420 –> 00:08:19.069
Vipul Vyas: in the scalable way. So that that’s not in the marketing world, obviously. But that gives you a sense of of kind of what what’s going on in marketing. I think it’s a little bit more

00:08:19.200 –> 00:08:31.589
Vipul Vyas: direct in that did the language and the words and the message you created drive more of the activity. You wanted more of the behavior you wanted

00:08:31.750 –> 00:08:43.099
Vipul Vyas: than before. So are you not just creating language faster, generating, content, faster. But are you generating? Better content that’s performing?

00:08:43.190 –> 00:08:45.670
Vipul Vyas: in the way that you want it to perform.

00:08:46.680 –> 00:09:00.049
[email protected]: And you know this is a topic that we discuss with clients all the time. But for the listeners, you know, Prisado is a very good example of a technology which provides both. We, we do provide more

00:09:00.090 –> 00:09:10.540
[email protected]: effective language generating an incremental amount of conversions. And we, you know, constantly aspire to be a net time saver

00:09:10.710 –> 00:09:14.019
[email protected]: for the marketing teams that we work with.

00:09:14.400 –> 00:09:18.510
[email protected]: We on the prior episode that we had a very

00:09:19.040 –> 00:09:21.739
[email protected]: interesting conversation around

00:09:21.910 –> 00:09:36.930
[email protected]: Generative AI’s role in augmenting jobs in the workplace. And we discuss the Goldman report saying that about 300 million jobs are going to be displaced by the end of this decade.

00:09:37.150 –> 00:09:47.889
[email protected]: What? What’s your take on Goldman’s findings and the potential for generative AI to either pivot some people’s jobs in the workplace or potentially replace them.

00:09:48.390 –> 00:09:53.170
Vipul Vyas: there will be dislocation. of different degrees.

00:09:53.450 –> 00:10:02.220
Vipul Vyas: and it and that transition change that any new technology and the disruption it creates is typically

00:10:02.770 –> 00:10:06.359
Vipul Vyas: There’s some discomfort associated with that.

00:10:06.480 –> 00:10:15.740
Vipul Vyas: Now you can do that smoothly. relatively, smoothly, over a long arc of time that can be absorbed by society and just human beings and their ability to adapt

00:10:15.940 –> 00:10:18.560
Vipul Vyas: And so if you look at

00:10:18.570 –> 00:10:25.420
Vipul Vyas: the Us labor force in the 18 nineties, early, 19 hundreds 80

00:10:25.440 –> 00:10:35.799
Vipul Vyas: percent of folks were employed in the agrarian space. You know, we farm farmer farm related occupations. And today it’s 2, I mean, very few people know a farmer.

00:10:36.320 –> 00:10:47.660
Vipul Vyas: so 2 of the population produces a food for the 98 of the people who aren’t in that space. when before, it was almost not quite the inverse but close.

00:10:47.710 –> 00:10:57.200
Vipul Vyas: you know. Pretty much everyone was involved in food production. And so that happened, if you look at graphs of how that transition occurred. It was slowly and linearly.

00:10:57.210 –> 00:11:05.779
Vipul Vyas: fairly, you know, steadily over the course of a century And so we absorb that relatively well.

00:11:06.080 –> 00:11:33.949
Vipul Vyas: The dislocation of increased urbanization and the implications of people getting off the farms. was managed in a way that didn’t create this much social strife. There is some, of course you know the the the sort of tale of the family farm and the, you know aggregation of agricultural and fewer hands. There’s no issues that people are concerned about there, but in general, from a social dislocation perspective, it was relatively

00:11:33.980 –> 00:11:41.949
Vipul Vyas: well absorbed. If you look at manufacturing in contrast that really started decline in the late sixties and seventies.

00:11:42.060 –> 00:12:01.250
Vipul Vyas: And there’s some specific reasons as to why people theorize that happened. the way it did. you saw a precipitous decline from, I think, in 27 ish, maybe a little over 30 of the labor force employed in manufacturing, and now close to at least in the 7% range today.

00:12:01.340 –> 00:12:03.619
Vipul Vyas: it may even have drift a little bit lower.

00:12:04.280 –> 00:12:17.389
Vipul Vyas: More recently. So that happened in the course of less than 50 years, and really accelerated in couple of phases the eighties, and then the 2 thousands. And it happened fast.

00:12:17.440 –> 00:12:24.430
Vipul Vyas: and that’s the you know what you have as a result of the rust belt. You know the cities that were impacted like Detroit.

00:12:24.450 –> 00:12:30.950
Vipul Vyas: and that didn’t happen smoothly. And that doesn’t mean that that can’t be mitigated.

00:12:31.060 –> 00:12:34.979
Vipul Vyas: if you look at Germany and Japan.

00:12:35.220 –> 00:12:51.709
Vipul Vyas: they actually really worked hard to make sure their manufacturing bases were at least somewhat secure and and tempered against some of the dislocation that was occurring, the Us. Being the manufacturing powerhouse coming out of World War Ii did not feel such

00:12:52.220 –> 00:13:03.710
Vipul Vyas: protection or protection, or it’s such mindfulness of that was necessary. We kind of took it. As for granted that we were that manufacturing powerhouse, and so

00:13:03.940 –> 00:13:08.440
Vipul Vyas: That decline was never buffered. So I say that to

00:13:08.470 –> 00:13:16.260
Vipul Vyas: point to the fact that AI probably will create this location. But it can be managed just like in the past. We’ve.

00:13:16.570 –> 00:13:28.099
Vipul Vyas: you know, there are examples of where forces that can lead to a severe dislocation can be mitigated. You just have to understand the threat, understand?

00:13:28.240 –> 00:13:32.170
Vipul Vyas: the so social issues it can create and then

00:13:32.470 –> 00:13:35.660
Vipul Vyas: make sure to invest to mitigate them.

00:13:36.790 –> 00:13:41.199
[email protected]: So I think that’s a it’s a good segue into it. Another part of your career.

00:13:41.230 –> 00:13:45.690
[email protected]: you are an adjunct professor

00:13:45.790 –> 00:13:51.019
[email protected]: at University of San Francisco. focusing on emerging technologies.

00:13:51.400 –> 00:14:03.249
[email protected]: What’s what’s the discourse in your classroom with your students? What are they? What’s top of mind for them as they contemplate entering or re-entering the workforce?

00:14:03.450 –> 00:14:09.079
[email protected]: And what What are some of the the the debates that you have among your students.

00:14:10.080 –> 00:14:12.689
Vipul Vyas: I think many of them want to understand, you know.

00:14:13.720 –> 00:14:28.309
Vipul Vyas: to the spirit of your last question, what’s the job market going to be for them? What’s it gonna look like? How’s it going to be different than when they first contemplated going to school? Because things change pretty rapidly in the public, you know Psyche and Zeitgeist

00:14:28.730 –> 00:14:33.139
Vipul Vyas: as of September 2022, when Chat Gpt emerged on the scene.

00:14:33.300 –> 00:14:37.109
Vipul Vyas: So a lot of them are, are thinking about. What are the implications for them personally?

00:14:37.480 –> 00:14:40.849
Vipul Vyas: I think this is one of those technologies that

00:14:41.060 –> 00:14:42.190
Vipul Vyas: will.

00:14:42.240 –> 00:14:46.879
Vipul Vyas: Our Ceos alluded to have this sort of hype cycle in the short term.

00:14:47.010 –> 00:14:59.269
Vipul Vyas: and then have more profound effects over the long term. you know. Travel agents that get put out of business overnight in the nineties. With the emergence of the Internet. there was sort of a sputtering along for a while until

00:14:59.510 –> 00:15:04.530
Vipul Vyas: it just didn’t make sense anymore. right? And I think a lot of

00:15:04.540 –> 00:15:11.729
Vipul Vyas: industries that are going to be disrupted by generative AI. It’s not going to happen all of a sudden. It’ll be slow. And then all of a sudden.

00:15:11.970 –> 00:15:19.990
Vipul Vyas: so I I think the real discussion is, how do people position themselves? Well.

00:15:20.070 –> 00:15:24.490
Vipul Vyas: to take advantage of the technology versus potentially get run over by it.

00:15:24.910 –> 00:15:36.700
Vipul Vyas: and I think that just as in manufacturing, which where automation actually had a huge impact on the rate of employment. If you could run the machines, you had a job

00:15:37.000 –> 00:15:49.660
Vipul Vyas: if you were the machine. less likely so and so there’s gonna be the need for human augmentation for at least some period of time into the foreseeable future. And so people who can

00:15:50.030 –> 00:15:54.549
Vipul Vyas: get the machine to do what we need to do.

00:15:54.670 –> 00:15:57.100
Vipul Vyas: And actually, it’s more going to be about

00:15:57.360 –> 00:16:02.479
Vipul Vyas: framing the problem. So people are going to be moving from production

00:16:02.530 –> 00:16:07.799
Vipul Vyas: to direction in terms of they’re going to be directors versus actors, if you will.

00:16:08.130 –> 00:16:19.380
Vipul Vyas: movie making analogy perspective. So instead of being the orchestrator, the actual final a leg of work, they’re going to be saying, this is the problem I want to solve.

00:16:19.490 –> 00:16:29.100
Vipul Vyas: This is how we want to solve it and then evaluating the solution. And so I think that is going to be a big change in people’s skills, that it’s going to be about defining problems.

00:16:29.160 –> 00:16:33.510
Vipul Vyas: well, in an articulate manner

00:16:33.570 –> 00:16:40.169
Vipul Vyas: versus the brute force solution that maybe where they’ve had focused in the past.

00:16:41.050 –> 00:16:43.169
[email protected]: I think that’s correct, and

00:16:44.130 –> 00:17:00.079
[email protected]: I’m observing that to in market, when I speak to clients. We, you know, at at Versato we’ve made a a push to to make part of our product product, offering more self-service, less reliant on client first party data.

00:17:00.290 –> 00:17:07.099
[email protected]: and I even heard a couple of our clients refer to as Prasado as an extra member of the creative team.

00:17:07.430 –> 00:17:12.060
[email protected]: And I really do think that the the dynamic that we see at play

00:17:12.109 –> 00:17:17.329
[email protected]: with enterprises who are implementing this technology effectively

00:17:17.450 –> 00:17:18.780
[email protected]: and efficiently.

00:17:19.619 –> 00:17:23.539
[email protected]: It’s not necessarily replacing human beings today.

00:17:24.040 –> 00:17:27.339
[email protected]: but rather helping undo riders block

00:17:27.530 –> 00:17:30.799
[email protected]: or help alleviate the blank page problem.

00:17:31.250 –> 00:17:41.959
[email protected]: And then when you take that and supplement it with the macroeconomic data that Prisado provides around hundreds of millions of consumer interactions.

00:17:41.990 –> 00:17:46.170
[email protected]: That’s where you add a statistical element to

00:17:46.270 –> 00:17:47.670
[email protected]: human intuition.

00:17:48.770 –> 00:18:06.830
[email protected]: So in your observations. companies that are getting this right today. what are they doing. and what is your advice to an executive who’s contemplating making their first investment into generative? AI for the enterprise?

00:18:08.720 –> 00:18:13.969
Vipul Vyas: It’s an interesting. It’s a tough question, because there’s

00:18:14.020 –> 00:18:17.579
Vipul Vyas: in this marketplace a few things are happening.

00:18:18.140 –> 00:18:26.249
Vipul Vyas: One, the core technology providers, such as Open AI, which is got a significant amount of funding for Microsoft, so it almost sort of commingled entities.

00:18:26.340 –> 00:18:32.739
Vipul Vyas: obviously came on the scene with Chat Gpt. several months ago, almost

00:18:33.650 –> 00:18:41.769
Vipul Vyas: a little bit over a half a year ago, I suppose? that’s now freely available to everyone.

00:18:41.810 –> 00:18:47.590
Vipul Vyas: And the core technology is also available. So you have as a function of that.

00:18:47.710 –> 00:18:50.970
Vipul Vyas: the proliferation of dozens.

00:18:51.060 –> 00:19:02.530
Vipul Vyas: probably more like hundreds of small companies that are all building point solutions on top of the core technology. Because now the barrier to entry.

00:19:03.110 –> 00:19:05.890
Vipul Vyas: to be innovative, to create something.

00:19:06.020 –> 00:19:19.339
Vipul Vyas: is much lowered because someone’s taking the hit on that big development effort, the big training model development transform all development machine learning efforts, all that big lyft. Someone else took

00:19:20.250 –> 00:19:27.299
Vipul Vyas: And now other people can leverage it to get in the market very quickly, and so they are. And so it’s almost like a

00:19:27.760 –> 00:19:33.199
Vipul Vyas: Brush fire came through. And now there’s a meadow full of flowers, just a lot of them to choose from.

00:19:33.410 –> 00:19:37.399
Vipul Vyas: But I think in terms of executives picking

00:19:37.860 –> 00:19:39.879
Vipul Vyas: the solutions that make sense.

00:19:40.960 –> 00:19:42.810
Vipul Vyas: The generalist

00:19:42.970 –> 00:19:50.570
Vipul Vyas: technologies are not going to solve specific business problems because they’re focused on being good at being general.

00:19:50.830 –> 00:19:53.620
Vipul Vyas: at offering a generic capability.

00:19:53.770 –> 00:20:03.299
Vipul Vyas: And they want ecosystems to point solutions because it helps them cheaply. Figure out what’s relevant in the market. Other people can go off and explore what use cases are

00:20:03.620 –> 00:20:06.120
Vipul Vyas: use or pertinent and

00:20:06.290 –> 00:20:09.370
Vipul Vyas: do all that heavy lifting for them.

00:20:09.660 –> 00:20:14.239
Vipul Vyas: and they can sit back. You know it’s the the whole gold rush analogy of

00:20:14.400 –> 00:20:28.660
Vipul Vyas: the people who supplied the pick. Axes were the ones that made the money, not the miners, necessarily themselves. And so I think the general technologies will still focus on the general technology, general tech, because there’s so much still to do there. There’s so much green field still the

00:20:28.990 –> 00:20:42.000
Vipul Vyas: to conquer and so point solutions will be the things that emerge that people have to evaluate because they’re focused on specific problems by their nature point solutions are attempting to solve specific issues or problems or challenges.

00:20:42.010 –> 00:21:02.950
Vipul Vyas: And so then it’s a matter of what point solutions are the right ones to pick. And so ones that actually are intimately familiar with the business problem that you’re trying to solve to some level of expertise of the processor space and that are focused on moving specific kpis because the end of the day you’re not buying generative. AI, you’re buying a business outcome.

00:21:03.360 –> 00:21:06.329
Vipul Vyas: It’s ideally what you’re buying. You’re buying a result.

00:21:06.500 –> 00:21:19.869
Vipul Vyas: And so looking back at other examples or other industries or other similar technologies where there’s a core technology provider set of providers have come on the scene that enabled a lot of other people to proliferate.

00:21:20.100 –> 00:21:24.870
Vipul Vyas: you see a few themes. One is that these point solutions to that 4 key characteristics.

00:21:24.940 –> 00:21:31.580
Vipul Vyas: One is that they have optimizer uiux for the specific

00:21:31.760 –> 00:21:33.930
Vipul Vyas: problem they’re trying to solve.

00:21:33.990 –> 00:21:43.230
Vipul Vyas: and that’s an artful effort, though not insurmountable. So just have I geared everything I’m trying to do

00:21:43.490 –> 00:21:54.029
Vipul Vyas: to make it as easy as possible for the end user to accomplish your tasks. And so then you at least get user, buy in. And the second thing is workflow integration.

00:21:54.100 –> 00:22:04.760
Vipul Vyas: It’s similar to uiux. Does this seamlessly flow into existing or adjacent processes, such that it’s not hyper disruptive to existing.

00:22:05.360 –> 00:22:33.159
Vipul Vyas: Work flows such that it’s hard to absorb That may change slowly over time. But initially, you want to fit into in a construct, people can get their head around so that the the technology can actually be successfully absorbed. So again, one is ui ux. 2 is a workflow integration. 3 is reporting. And that’s really the way that the vendor justifies their invoice. But it’s also a way that you understand. That is this solution actually moving

00:22:33.280 –> 00:22:36.699
Vipul Vyas: the metric, that it says it was going to move, that it was going to move.

00:22:37.040 –> 00:22:53.449
Vipul Vyas: And so reporting and robustness of reporting and the insights you can give are gonna be big, and those are solutions specific, not general or generic. You know that that typically won’t come from a generic provider. And on the fourth thing is that they’ve extended the technology. So in this case they’ve extended language models.

00:22:53.590 –> 00:22:58.360
There’s a litany of previous examples that are illustrative of this

00:22:58.450 –> 00:23:25.069
Vipul Vyas: from speech to recognition technology that I’m familiar with. Where, if you want to do a driving directions. Application. The uiux had to be optimized, that for maps, the workflow integration, and how to integrate with like GPS ships and whatnot which the generic speech. Recognition applications would not bother with like why would they? They had to actually report on terms of accuracy and get that feedback loop going, and then a fourth. They had to do novel things such as disambiguate

00:23:25.100 –> 00:23:39.529
Vipul Vyas: words like Marquette Street and Market Street M. A. R. Q. E. T. E. Versus M. A. R. Ket, and they have to use traffic data to figure out probabilistically if they both sound similar when someone says them.

00:23:39.600 –> 00:23:50.599
Vipul Vyas: which one is it more likely going to be to to disambiguate with me, too? So that to extend the core technology to address the specific problem, such as an example, you know, in the case of

00:23:50.620 –> 00:24:04.989
Vipul Vyas: Prisado, we’ve had to actually take language and actually inject emotion into it. and then couple that with, you know a clear description of a call to action, to then couple of motion with motivation to actually drive

00:24:05.280 –> 00:24:18.460
Vipul Vyas: action. And the behaviors we want. So Prisado is another just case, study of solution in the space with generic capabilities, generic technologies where the point solution has to extend

00:24:18.570 –> 00:24:24.800
Vipul Vyas: what the core solution the core technology does to solve a business problem in this case, driving conversions

00:24:24.830 –> 00:24:29.960
Vipul Vyas: and the the the behaviors we want from consumers. So

00:24:30.230 –> 00:24:34.820
Vipul Vyas: that’s the theme. You can see that with voice by metrics

00:24:34.920 –> 00:24:41.430
Vipul Vyas: as well in in this for another form of gender value people don’t have to think about is a path planning with

00:24:41.590 –> 00:24:53.290
Vipul Vyas: actual autonomous vehicles. The Path plan is actually a form of generative AI and you have different applications in different scenarios from the 18 Wheeler versus an autonomous

00:24:53.510 –> 00:25:04.789
Vipul Vyas: bus system that they’re now rolling out in different cities. So yeah, I I think the the executives are going to think about making bets on technologies. The good thing is that you know.

00:25:04.950 –> 00:25:08.630
Vipul Vyas: these bets aren’t sealed in cement

00:25:08.750 –> 00:25:12.690
Vipul Vyas: but you look for some hallmark characteristics

00:25:12.750 –> 00:25:16.060
Vipul Vyas: of winners. We’re likely winners

00:25:16.260 –> 00:25:21.829
Vipul Vyas: based on some of the attributes I just mentioned. And that’s based on my, my.

00:25:22.010 –> 00:25:35.169
Vipul Vyas: you know, sort of set of characteristics I I went through is just based on historically or the last 20 years. Seeing similar trends play out where a general technology came on the scene. And then a bunch of point solutions emerged.

00:25:35.360 –> 00:25:39.360
Vipul Vyas: There’s a whole cacophony of activity, and then a few

00:25:39.670 –> 00:25:41.700
Vipul Vyas: few remain standing at the end.

00:25:41.850 –> 00:25:46.190
Vipul Vyas: and it was the ones that have those 4 characteristics I mentioned more often than not.

00:25:46.290 –> 00:25:51.520
[email protected]: It’s very reminiscent vipple if you wind the clock back about 1015 years.

00:25:51.640 –> 00:26:02.470
[email protected]: Large enterprises were either an sap shop or an oracle shop, both of which had Hr. Functionality, finance, functionality, procurement.

00:26:02.730 –> 00:26:12.460
[email protected]: And then, with the advent of software as a service and cloud providers, you had what was referred to back in the day as best of breed.

00:26:12.780 –> 00:26:17.810
[email protected]: and I remember in at the beginning of my career, selling into these companies

00:26:17.870 –> 00:26:30.620
[email protected]: there was a philosophy of. You’re either a loyalist to one of sap or oracle, and you buy every department of functionality or best of breed.

00:26:30.680 –> 00:26:40.659
[email protected]: and you have the core erp that would api into cloud solutions which back in 2,008, 2,010,

00:26:40.900 –> 00:26:45.990
[email protected]: didn’t necessarily have their own analytics and reporting functionality.

00:26:46.170 –> 00:26:50.939
[email protected]: had limited rep in terms of their capabilities

00:26:51.260 –> 00:27:04.679
[email protected]: fast forward to today. A lot of those point solutions work day being a notable example. Those run entire large organizations. And I think it’s a actually a pretty interesting parallel

00:27:04.880 –> 00:27:12.119
[email protected]: between an open AI and Sap general technology that can be used to satisfy

00:27:12.200 –> 00:27:14.300
[email protected]: repeatable business functions.

00:27:14.420 –> 00:27:18.720
[email protected]: and then more bespoke. Lms will be built either on top of.

00:27:18.830 –> 00:27:30.830
[email protected]: on top of those language models or in conjunction with them. and I do think that it will follow a very similar technology adoption arc that the Erps and and best of breed solutions followed about 15 years ago.

00:27:31.640 –> 00:27:32.709
Vipul Vyas: You’re probably right.

00:27:33.400 –> 00:27:36.809
Vipul Vyas: all right. I think that The other thing that will

00:27:37.660 –> 00:27:38.820
Vipul Vyas: follow.

00:27:39.010 –> 00:27:43.600
Vipul Vyas: I suspect, is going to be along the lines of

00:27:43.770 –> 00:27:50.609
Vipul Vyas: you’re gonna have generative AI everywhere, especially from a marketing perspective. You’re gonna have generative AI and all the point solutions

00:27:50.690 –> 00:28:01.450
Vipul Vyas: or at the all the different points. channels, specifically. And so you’ll have an SMS generative AI solution. You’ll have one for email, one for web

00:28:01.760 –> 00:28:12.640
Vipul Vyas: and so you can have lots of different potential generative AI solutions to pick from provided by your existing vendors for core messaging technologies.

00:28:12.900 –> 00:28:24.450
Vipul Vyas: And so what may happen is that each one of these is going to have a different voice based on the people that are in charge for that particular channel within the enterprise

00:28:24.620 –> 00:28:33.270
Vipul Vyas: based on the vagaries of that particular generative AI solution. And so you’re going to be communicating different messages

00:28:33.650 –> 00:28:37.019
Vipul Vyas: to different, to to people at different times.

00:28:37.130 –> 00:28:40.680
Vipul Vyas: because you have this sort of

00:28:41.290 –> 00:29:01.589
Vipul Vyas: menagerie of different generative AI solutions. This happened with Crm many, many years ago, where once people knew they could use the SMS tools they could use email, they could use different ways to communicate to customers. Everyone kind of went nuts and started sending customers, bunch of SMS messages, a bunch of emails, a bunch of

00:29:01.700 –> 00:29:05.529
Vipul Vyas: I know, even in some cases

00:29:05.710 –> 00:29:13.279
Vipul Vyas: printed material. and there was no one sort of coordinating what messaging was going to the customer.

00:29:13.560 –> 00:29:15.859
Vipul Vyas: and what made sense to prioritize. And what

00:29:16.550 –> 00:29:29.900
Vipul Vyas: ways we could. You know what what mechanism we could use to keep someone from overwhelmed so that they we diluted our own messaging. That’s when Crm really came on the scene, right to basically be a coordination function. To say, I want to be air traffic control.

00:29:30.030 –> 00:29:40.499
Vipul Vyas: to coordinate what is sent, to whom, when. and how often. and that was basically an attempt to rein in the madness.

00:29:41.050 –> 00:29:59.629
Vipul Vyas: I literally, at 1 point, many, many years ago got a SMS, a paper letter and an email from the same company on the same day, wanting to do 3 different things. And so this is again exactly what Crm was trying to solve. And I think nowadays what we’ll want to head off at the past is

00:29:59.860 –> 00:30:01.650
Vipul Vyas: different language

00:30:01.660 –> 00:30:06.980
Vipul Vyas: being used for a person in different places

00:30:07.460 –> 00:30:16.989
Vipul Vyas: and a lack of consistency and a dilution of brand identity. and also,

00:30:17.050 –> 00:30:22.810
Vipul Vyas: you know, some people call it brand voice, but also just the ability to

00:30:24.140 –> 00:30:30.169
Vipul Vyas: really distinguish the company and also orient the customer. As to here’s what they can expect

00:30:30.180 –> 00:30:32.159
in terms of how we communicate.

00:30:32.480 –> 00:30:35.590
Vipul Vyas: because there is a single point

00:30:36.050 –> 00:30:41.399
Vipul Vyas: of coordination. And and Prisado can play that role of a essentially a language hub

00:30:41.430 –> 00:30:56.230
Vipul Vyas: or air traffic control. To say, you know, here’s the kind of language that resonates with this person at this time in this context. And so we should use across channels to essentially at the end of the day. You’re trying to always drive a couple of things. you’re trying to influence behavior

00:30:56.280 –> 00:31:01.939
Vipul Vyas: and drive loyalty. And so you’re not gonna be able to do that with

00:31:02.160 –> 00:31:05.539
disparate messages that aren’t very well

00:31:05.620 –> 00:31:07.600
Vipul Vyas: coordinated across platforms.

00:31:08.430 –> 00:31:20.449
Vipul Vyas: And that’s one thematic issue that I think people in marketing specifically should worry about is that now we have 15 generative AI solutions, and they all speak to our customers in different ways.

00:31:21.870 –> 00:31:25.410
Vipul Vyas: And then also our customer here is 15 different ways of us talking to them.

00:31:26.790 –> 00:31:37.989
[email protected]: So on the topic of air traffic control. I know you. You recently attended. I believe it was a Congressional hearing on AI

00:31:38.810 –> 00:31:49.549
Vipul Vyas: any main takeaways you want to share from that with the listeners.

00:31:50.100 –> 00:32:02.969
Vipul Vyas: They didn’t head off the social implications of social media in time they didn’t understand or grasp the unintended consequences, or the natural

00:32:03.570 –> 00:32:10.549
Vipul Vyas: progression of what would follow the implicate natural implications of something like social media because they didn’t bother to step back and understand them.

00:32:11.240 –> 00:32:15.670
Vipul Vyas: and so they don’t want to make the same mistake twice.

00:32:16.030 –> 00:32:28.470
Vipul Vyas: As a result, I think AI is going to get a lot more scrutiny from a political perspective because of its clear. I mean, you don’t have to imagine you can

00:32:28.760 –> 00:32:34.429
Vipul Vyas: feel it, that you can see and sense it, that it’s clear potential impact on society.

00:32:35.120 –> 00:32:39.289
Vipul Vyas: And so the big takeaway to summarize that is that

00:32:39.590 –> 00:32:44.980
Vipul Vyas: the political world will be active and much more engaged with generative AI.

00:32:45.280 –> 00:32:52.700
Vipul Vyas: Then it has been with technologies in the recent past. I think Italy potentially banned a lot of.

00:32:52.780 –> 00:32:56.819
Vipul Vyas: you know, chat gpt usage already as a country

00:32:56.920 –> 00:33:00.810
Vipul Vyas: enterprises have also done the same thing.

00:33:01.170 –> 00:33:03.279
Vipul Vyas: So

00:33:03.340 –> 00:33:09.030
Vipul Vyas: I know that’s not governmental, but I think that’s going to be the theme is that you’re gonna have a very active

00:33:09.270 –> 00:33:13.300
Vipul Vyas: political response to anything that’s happening in the space

00:33:16.220 –> 00:33:25.619
[email protected]: that that makes sense. Well, that I know we’ve we’ve covered a lot of ground today and thank you. You’ve been a tremendous guest. I have one final question for you

00:33:26.290 –> 00:33:33.000
[email protected]: of all of your observations in the market everything that you’re working on with Prisado.

00:33:33.110 –> 00:33:39.469
[email protected]: What would you say you’re looking forward to the most in this space the next 6 to 12 months?

00:33:41.230 –> 00:33:52.529
Vipul Vyas: I know we spoke a little bit about potential, you know, social doom and gloom here and there, but I think that the other part the other side of the coin. Is that

00:33:53.070 –> 00:33:58.300
Vipul Vyas: all the creativity that is trapped in many, many people’s heads

00:33:58.540 –> 00:34:00.580
Vipul Vyas: for want of a way to express it.

00:34:00.630 –> 00:34:10.409
Vipul Vyas: because they can’t get it out, because, you know, they can’t draw. They can’t write, maybe, as well as they think they should be able to write all the things, all the creativity

00:34:10.449 –> 00:34:13.499
Vipul Vyas: that is locked away in humanity

00:34:13.560 –> 00:34:18.190
Vipul Vyas: now has a mechanism by which to express itself.

00:34:18.320 –> 00:34:27.630
Vipul Vyas: you know, if you were wanted to create an application. you need to learn how to code. There was a inherent barrier for you.

00:34:28.880 –> 00:34:35.290
Vipul Vyas: expressing and bringing to reality what you wanted to bring to reality. Now.

00:34:35.370 –> 00:34:44.319
Vipul Vyas: I mean code is an abstraction. Right? Basically, ultimately, code is just computer programming code. is just an an abstraction layer above

00:34:45.400 –> 00:34:48.939
Vipul Vyas: turning on and off a bunch of transistors on a on a chip.

00:34:49.210 –> 00:34:50.280
Vipul Vyas: Ultimately.

00:34:50.320 –> 00:34:54.389
Vipul Vyas: it’s just different layers of getting that now

00:34:54.570 –> 00:34:57.989
Vipul Vyas: simple spoken words, normal language

00:34:59.290 –> 00:35:01.610
Vipul Vyas: can effectuate that

00:35:01.680 –> 00:35:05.609
Vipul Vyas: you can actually describe. I want a program that does Xyz

00:35:05.980 –> 00:35:09.549
Vipul Vyas: as an example. And generally I can create such a thing

00:35:10.570 –> 00:35:15.169
Vipul Vyas: right? And so I look at that to say, there’s a lot of trapped

00:35:15.370 –> 00:35:26.610
Vipul Vyas: creativity that for simple sources of friction that have historically exist couldn’t get out. Now, it can. Same thing on the marketing front. We, wanna

00:35:26.740 –> 00:35:33.120
Vipul Vyas: you know, as you mentioned, there’s a plenty of things that never happened because of riders block so many things that didn’t happen. Well.

00:35:33.400 –> 00:35:38.160
Vipul Vyas: because I just didn’t know certain things that now are knowable.

00:35:38.980 –> 00:35:48.160
Vipul Vyas: And I think, as I alluded to before, when you have that ultimate nexus of efficiency and efficacy, that wonderful

00:35:48.320 –> 00:35:52.390
Vipul Vyas: confluence of the 2 you remove constraints. Now.

00:35:52.720 –> 00:36:03.629
Vipul Vyas: things you can’t even imagine were possible before such as speaking individually to Alex in the way Alex wants to be spoken to in a way that’s compelling to him

00:36:04.580 –> 00:36:12.560
Vipul Vyas: is possible because I can create copy on the fly images on the fly, video, on the fly. all contextually

00:36:13.610 –> 00:36:14.770
Vipul Vyas: modified

00:36:15.790 –> 00:36:21.609
Vipul Vyas: that before you know, just a concrete example, if I want to shoot a video, I’ve got a

00:36:21.830 –> 00:36:38.539
Vipul Vyas: get studio time. You gotta get actors. Gotta get. You know, script written. Get all that stuff done. Get a basically shoot this thing do a lot of post production editing. It’s a whole thing. So you better make sure that video generically is resonating with as many people as possible.

00:36:38.600 –> 00:36:39.670

00:36:39.810 –> 00:36:44.430
Vipul Vyas: the constraints that force that are gone. So actually, I can create

00:36:44.500 –> 00:36:56.170
Vipul Vyas: interactive and engaging material and content for a person. In this case you know yourself, Alex. dynamically, on the fly. for essentially close to nothing

00:36:56.180 –> 00:36:57.450
Vipul Vyas: in terms of cost.

00:36:58.800 –> 00:37:03.260
Vipul Vyas: and the that settled to it better, faster, cheaper, and that

00:37:03.460 –> 00:37:15.059
Vipul Vyas: fundamentally different way of engaging people. you know, much more personalized way that’s going to resonate with them is just going to be different. I mean, it’s gonna open up an entire new

00:37:15.180 –> 00:37:20.290
Vipul Vyas: way of people interacting with enterprises, people interacting in general.

00:37:20.540 –> 00:37:25.749
Vipul Vyas: So I think those are the kind of interesting, positive things I think, that are on the near term horizon.

00:37:25.860 –> 00:37:28.499
[email protected]: It’s it’s fascinating. It is such a

00:37:28.860 –> 00:37:36.109
[email protected]: watershed moment. In my opinion, I know you. You mentioned some others. I’ll summarize them for the listeners. Now.

00:37:36.560 –> 00:37:39.249
[email protected]: I I I do think, sequentially

00:37:40.430 –> 00:37:46.309
[email protected]: manufacturing the computer being invented. the Internet proliferating.

00:37:46.500 –> 00:37:55.799
[email protected]: I do think that we are looking at another moment with the advancement of generative AI that will have the ability to transform the way we work.

00:37:55.890 –> 00:37:58.319
[email protected]: transform the way we live, the way we learn.

00:37:58.590 –> 00:38:05.899
[email protected]: And I appreciate that all of the the examples that you’ve given. you know you’ve you’ve had a front row seat

00:38:05.990 –> 00:38:09.969
[email protected]: to this industry for the better part of the last 20 years.

00:38:10.210 –> 00:38:28.770
[email protected]: and finally the the wealth of knowledge you’ve been sharing with Prisado’s clients. Now, our listeners will be able to benefit from So Vit bull, thank you very much. for joining the podcast again. This was vipel viaas. He is senior vice President of go to market strategy at Prisato.

00:38:28.810 –> 00:38:30.050
[email protected]: Thanks again before

00:38:30.180 –> 00:38:40.929
Vipul Vyas: thanks, Alex. I’ll I’ll not let you stop me there. I’ll say 2 more quick things in terms of one of the things that buffered manufacturing decline and job creation was actually the rise of the It sector.

00:38:41.580 –> 00:38:56.689
Vipul Vyas: which actually created jobs that people that no longer going to manufacturing can now assume to some degree. So that was a a safety net of some degree. So we’ll need something like that. And the second is, you know, another big technology trend

00:38:56.750 –> 00:39:06.540
Vipul Vyas: that didn’t quite take office people into it was blockchain, as example. And I think actually, it will have new relevance, because you can imagine scenarios where

00:39:06.600 –> 00:39:12.430
Vipul Vyas: a company’s enterprise system is a negotiating with another company’s enterprise system or 2

00:39:12.530 –> 00:39:22.190
Vipul Vyas: AI bots are actually interacting with with each other, and the resolution of the interaction, because it’s now no one entities

00:39:22.870 –> 00:39:28.310
Vipul Vyas: ownership of that. The final adjudication can leverage things like blockchain to track

00:39:28.550 –> 00:39:39.349
Vipul Vyas: the outcome of such interactions. So that may actually, a technology, that sort of came and lost a little bit of luster recently. we actually find new life.

00:39:39.550 –> 00:39:51.009
Vipul Vyas: funny enough in this generally, I boom, but I’ll leave it at that. thank you for having me. I I definitely appreciate it.

00:39:51.530 –> 00:40:03.840
[email protected]: Sectors within technology that we left unattended to. So I’m glad that we we got him in up a bit. But thanks again, first of many, hopefully. And I’m sure the listeners are really gonna appreciate your insight.

Subscribe to our newsletter for the latest news & information.