Pod Insights MarTech Podcast ™ // Marketing + Technology = Business Growth Developing & Deploying AI-Powered Apps Successfully About this episode Senior Product Marketing Manager for AI at Progress, Philip Miller, delves into the successful development and deployment of AI-powered apps. Progress, a company empowering organizations to navigate disruptive change, enables the creation of responsible AI applications with ease. With over 4 million developers relying on Progress, Philip discusses key insights for implementing AI solutions effectively in marketing strategies. Show NotesConnect With: Philip Miller: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Transcript 0:00 Speaker 00 The Martech Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth, and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iHereEverything.com.
0:25 Speaker 01 From advertising to software as a service to data, across all of our programs and clients, we've seen a 55% to 65% open rate.
0:36 Speaker 02 Getting brands authentically integrated into content performs better than TV advertising.
0:42 Speaker 03 Typical lifespan of an article is about 24 to 36 hours.
0:46 Speaker 01 If we're reaching out to the right person with the right message and a clear call to action, then it's just a matter of timing.
0:53 Speaker 00 Welcome to the Martech Podcast, a member of the I Hear Everything Podcast Network. In this podcast, you'll hear the stories of world-class marketers that use technology to drive business results and achieve career success. Here's the host of the Martech Podcast, Benjamin Shapiro.
1:15 Speaker 04 Welcome to the Martech Podcast. I'm Benjamin Shapiro, the executive producer of the Martech Podcast, and today we've got a special episode for you, which is going to be guest hosted by Juan Mendoza, the author of the Martech Weekly Newsletter. Juan is a recovering martech consultant turned creator who writes an amazing weekly newsletter about the martech industry, and I'm thrilled to invite him and some of his friends to take the mic and share their knowledge with you, our loyal Martech Podcast listeners. All right, here's a special episode of the Martech Podcast, guest hosted by Juan Mendoza, the author of the Martech Weekly Newsletter.
1:51 Speaker 05 Welcome back, MarTechers. My name is Juan Mendoza, your guest host on the MarTech Podcast and the CEO of the MarTech Weekly, a MarTech nerd who is excited to talk about AI and how it's hallucinating in the marketplace right now. Joining me today is Philip Miller. He's the Senior Product Marketing Manager for AI at Progress. Progress is innovating at the edge of disruptive change in generative AI, large language models, and the real sort of bleeding edge of technology in using machine learning and other AI models. Relied upon by over four million developers and technologists worldwide, Progress empowers organizations to
2:33 Speaker 05 develop, deploy, and manage AI-powered applications and experiences with agility and ease. All right, MarTechers. Yesterday, we talked about the weird and wonderful world of AI hallucinations. Today, we're actually taking a different approach to AI and thinking about the concrete, practical ways in which companies are actually embedding this technology in their companies, their marketing plans, their strategies, and their digital transformation efforts as well. So Philip and I are going to continue our conversation from yesterday by discussing developing and deploying AI-powered apps successfully. Philip, welcome back. Thanks for joining us and coming back again to the studio.
3:12 Speaker 06 No problem at all. Thanks for having me. Great to talk so far. I'm really interested in today's chat.
3:16 Speaker 05 I am interested because I think this is the pointy end. There have been some questions out in the marketplace. You've had investment firms, you've had multiple banks actually come out and say, isn't there really multiple billions, hundreds of billions of dollars of value in this generative AI stuff? We've kind of come off that crest of hype where it's all cool, it's all fabulous, it's all transformative, and it's all amazing, to now being more scrutinizing to the technology, more critical. How much value actually is there for us to realize? I actually think for generative AI and large language models right now, the valuable use cases are in marketing, and they're also in development and engineering and technology coding, basically. So because they're both very language-focused professions, marketers have to deal with words. Go figure. They have to write.
4:01 Speaker 05 They have to create imagery, they have to communicate in the same way that a developer needs to communicate to a computer in order to develop applications and software and so on so forth. And so when we come to thinking about this pointy end, which is you've got these options around you, new companies starting every day, and your enterprise wants to start thinking about integrating generative AI into your business practices, what would that actually look like to create value in your company? So, big topic, a lot to talk about, but maybe we can talk a bit about your experience here, even at Progress, on how you've been working with businesses to solve that, which is really how do you embed AI into companies?
4:39 Speaker 06 I'll try and illustrate it through an example. We've been talking to our customers now for many years about having a unified data platform in their business, bringing data from multiple different sources together, curating, harmonizing, connecting, governing that data, applying various security policies to that data, and being able to search that in a repository that can scale with their organization. So when AI came about, a lot of the groundwork for what was seen as well as solution to what we talked about yesterday, hallucinations, that retrieval augmented generation architecture, that groundwork was exactly that, that connected contextualized data platform.
5:21 Speaker 06 Okay, now you've got that, now you've got really good data. And what we found was, we developed our RAG-based architecture roughly around the beginning of summer last year. And by the time we'd come out of summer and started approaching some of our larger customers where we knew that they were sort of experimenting in this place, one of them had developed the architecture completely independently of us, which is great for us, because it was like, oh, this is the path for least resistance, this is great. But what they did is they took that connected contextualized data in that data platform, and they started viewing AI as a platform, rather than a point solution in their business. They looked at the use cases that might be available for that
6:04 Speaker 06 platform. And then they started to look at where could they add value most? Now, they created a smarter content Authoring tool that allows them to, as they're typing, it goes, oh, I think you're talking about this legislation or this particular thing. And it will then bring that into the document for them. It might say, oh, you're talking about this, here's some suggested images. It can do all of that because it's constantly querying the database to say, please deliver me this. Now, just that application alone saves that customer from them. And they've said, this is conservative numbers. Their seven and a half thousand employees saves them two million dollars a week. And then they were like, wait a minute, with generative AI, I could go further with this content creation.
6:48 Speaker 06 I could really enhance what they're delivering. I could give it sound lights. I could say, oh, you're talking about this, maybe you should include this. So now they're integrating Gen AI into that tool and that value is going even quicker. And they're not stopping there. They're going, because we have this platform, because we're viewing AI as a platform. We are, they're looking at compliance solution. They are looking at adding it to their microservices platform to open it up to other use cases and applications in their business. They're really taking AI as a platform, connected, contextualized data, supporting that as a platform to that next level. And it's fascinating to see the results that they're getting. And these documents are going to customers, so they have to be correct.
7:34 Speaker 06 And with the retrieval augmented generation architecture that they're using with our technology, they're getting really good results from this. And it's not something that's taking months, years to deploy. It's taking weeks. One customer we approached with, we presented the idea to them. Three weeks later, we came back and went, what do you think about the idea? We think it's really good. We've put it into production. And now they have a international intelligent knowledge hub that is supported by our technology underneath it. That goes across all of their global workers. And it's really interesting to see how agile and how quickly they're developing these solutions. But again, it comes down to, okay, where's the lowest hanging fruit?
8:19 Speaker 06 Marketing is a great place to start with these tools. Because we are generating so much content. We're generating white papers. We're generating webpages. We're generating applications for people to engage with. We're informing our messaging internally and so on and so forth. I mean, I use generative A I all the time as a soundboard. I say, oh, what about an idea with this thing and that thing? Can you give me like ten and a title for a blog or something? Or I want to do this post about this thing. Can you give me a start for ten on how you would summarize this document, say, for instance, so I can put it on or whatever. I have to change it. The like, the output is never a hundred percent, but then my output when I first begin is never a hundred percent. So it's a really interesting time in the marketing space.
9:04 Speaker 06 And I think that looking at A I as a platform rather than a point solution for your business will help you have that growth mentality, that agile iterative approach to application development that you need in space. Our solution is model independent. We don't have to go to Chat G P. I could go to Claude or I could go to, you know, Anthropic or whoever it might be. And that's really important because these models change really quickly. So being agile, being independent of particular A I technologies allows you to take advantage of the latest thing. Also, it allows you to use right A I for the right use case.
9:45 Speaker 06 Chat C G B is great, in my opinion, for writing down LinkedIn posts or whatever it might be. But does it have the best image generation or video generation? Are there better models out there that you could use to power that in your business? So then you model independent for the application or the use case that you're looking at.
10:06 Speaker 05 I think there's this interesting paradigm you're trying to sort of shift here, which is a platform versus point solution. Now, a platform is obviously very different from just bringing, say, Chat G P T or a single tool into your business, right? It's like one person, maybe a small group of people, maybe using it for a very low risk, very specific thing. But what you're saying is investing in a platform here, is that the idea is that it would actually enable Multiple use cases as they come up because people and companies tend to often bring in software all the time for random, random things, you know, like, you know, from like, oh, we need to do this specific task. All right, let's just get a software tool to do that thing, right? Oh, we needed this to hire some people and now we don't need anymore. Okay, get rid of it.
10:48 Speaker 05 And there is this transient nature of app, software apps in enterprise companies, right? But from what I gather, you're actually saying that the platform approach is better because it could actually service multiple use cases as they come up. But you have the sort of governance and the standards and the ways of approaching this kind of set in the business. So you have guardrails.
11:07 Speaker 06 Exactly that. You're taking enterprise features of governance and control and security. You're applying not just to the individual, say, like your user profile, but you can apply it to individual queries. So you could say, if somebody searches this, don't return anything. Or if the prompt says this, and this is another way of ensuring you have robust, scalable and accessible AI for people, is in that response, it could give PII information. There's cases out there where it does that. By using our technology, the way that we set that up, we can say, well, this is PII. We know this is PII because I can just put it through my data platform and identify that and say, right, don't show that to the end user. So they never do that in
11:50 Speaker 06 the response. So you are securing your data both to and from the AI. That platform approach is being applied at a lot of different organizations and we're already seeing the results against traditional application-driven enterprise technology. For instance, it was just a few weeks ago now where Klarna, a payment provider here in the, um, I mean, I don't know whether you've got it in Australia, they have chosen AI in their business. They have deployed a chatbot that has handled 2.3 million customer service chats in 35 different languages globally in just four weeks.
12:31 Speaker 05 It is a very interesting time and I think that what you're saying is that, well, it is a joke to reflect on what you're saying here, which is you can take the marketing technology landscape diagram, which is this 14,000 apps on a page, right? And it's like all the different categories, email, CDP, all the things. And then you can just put the OpenAI logo over the top, basically, you know, because all of the software that we use in marketing and business is just language, just code, software, right? It's just code written on code, written on code. And so why wouldn't you have the ability in the future to actually just use these tools to, well, using AI and large language models to build that thing for you or send, you know, I want to send an email campaign. Why do I need to step through a clunky interface in Salesforce Marketing Cloud when I can just ask it to create it for me?
13:14 Speaker 05 And it comes back as an agent and then does that work for you? And we're starting to see that we are starting to see some marketing technology platforms move in that direction where you set parameters, you set guardrails and you give it some flexibility, but then it will go and optimize the right message, the right content to a customer when it's needed. And it's one of those things. Well, what I think is quite interesting, I think there's sort of two bear cases or two criticisms I have. And I'm curious on what your thoughts are on both. My first criticism is that in the world of marketing, abstracting, automating, anything that sort of touches a customer will distance you from that customer. It puts another sort of software layer, even if it's an AI tool in between your customer. Yes, there is things like you can get better insight and you get faster insight using LLMs for querying data and to do analysis.
13:59 Speaker 05 Yeah, I get that. But on the flip side, if you're letting AI automate a lot of your customer experience, then you said it before, it actually can be a black box. You don't know exactly what's going out because everything's individualized for customers. So that's my first criticism. My second criticism is that because of using these tools, are we going to get marketers to just over time get worse at their job? Because they offload a lot of the thinking to an AI tool. And I'm sure you get this criticism a lot, but I think there's an interesting angle here, which is, well, a chat GPT, write a marketing strategy for me. Okay, form it into a PowerPoint presentation, change some numbers around and then go and pitch to the executives for your next business case. I can see millions of marketers doing that because guess what? People are lazy and they want an easy way to get.
14:41 Speaker 05 Stuff done, people are busy. So that's kind of my thought process is that those are sort of two mainstream criticisms. The one is it takes you away from your customer. The second one is that it actually diminishes your ability to do the work yourself, which then diminishes your judgment and your ability to think. Over to you, what are your thoughts? Which one would you like to tackle first?
14:59 Speaker 06 The second one first, which is around AI, human, is it gonna make us less intelligent? You're not gonna be able to do things, so on and so forth. When we wanted to move large objects, we used the wheel. That didn't make us lazy, it just allowed us to do new things with new new objects. When calculators were first introduced, they were like, oh my god, kids aren't gonna know maths anymore and they're not gonna be able to do these sums. What we actually found is, in the field of mathematics, which is very much adjacent to AI, we actually improved our maths because we leveraged these tools in new ways to help us solve some fundamental mathematical problems.
15:41 Speaker 06 The tool doesn't make you lazy, it doesn't make you stupid. It's how you use that, is what you use it for. And people will change what they do in their jobs. Now, this change is gonna come quick, it's probably gonna come quicker than any other change in human history. I like to describe this as we are moving into an epoch, a new epoch within human history. We had the farming revolution, we had the industrial revolution, the Renaissance, so on and so forth. This is a new epoch that we're moving into.
16:20 Speaker 06 And how we use those tools will inform our output and what we do, that what we do with them will change. And fundamentally, and this goes to an Oxford study that I read about, where ninety percent of jobs are gonna be impacted just by Gen AI, let alone AI in general, by 2032. Ninety percent of the roles that we do are gonna be impacted in one way, shape or form. They're gonna change what we do, but they're gonna offer new opportunities. They are going to help us. Generate new roles, completely new fields in some cases for people to engage with. So that's my AI optimistic slash realism.
16:59 Speaker 05 But you are right that the historical precedent is that the more we automate a lot of the thinking work, the more high abstract thinking we can do, which is kind of, at least, and that's kind of what has led us. Computing has led us to AI, then AI, what does that lead us to next? It's actually, yeah, you're right that there's this historical precedent for the tools, even though they take away a lot of human effort, they actually play another role, which is exponentially increasing the amount of effort you can apply to work as well. So that is a very interesting point of view. The second, well, the first point is about, well, abstracting away from customers. Now, I just think that we have this conundrum in enterprise, particularly large companies, where marketers are so distant from their customers already. They don't spend a lot of time in the cold phase. They don't spend a lot of time actually interviewing customers.
17:44 Speaker 05 A lot of it is abstraction upon abstraction. Even the way of doing data analysis is reducing customers' interactions into numbers, which sometimes cannot be helpful when it comes to marketing. But how do you think a large language models can kind of help? Bridge a better understanding of customers and a better insight into what customers are doing to enable growth and better experiences?
18:06 Speaker 06 I think we answer the question ourselves really easily in terms of you just said, marketers don't spend a lot of time with customers. Well, that's because they're busy generating content. They are generating new material. They are generating strategy and so on and so forth. Well, if I start using AI to accelerate that process, maybe get better results, maybe get better messaging, maybe increase my messaging reach, then I've got more time to spend with customers. I love talking to customers. I love talking to our partners. I love talking to industry people about this because it gives me new ideas. It gives me new ways of thinking and new ways of considering the customer's point of view on our technology, on the trend in general. And AI will be able to afford you to do that.
18:50 Speaker 06 The other side of that is from the end user themselves. When people engage with a company, they are looking for a result. They are looking for some sort of endpoint to the. Engagement. Now, from a customer service perspective, I want an answer to my question. Why is this not working? Or how can I fix this? Or whatever it might be. If an AI can give me that answer, that's cool. I just go and press the on off button or whatever it might be that it tells me to do. If it needs that elevated, more abstract view, then I can get there quicker by using those AI engines. Cause they can then tell me, I don't have the answer. This can't be done by me. You need to speak to our next level of support.
19:31 Speaker 06 So actually, you're moving customers quicker to a positive resolution. Or you are getting them quicker to actually engage with you as an organisation. Because they can quickly go, okay, this is where you need to go. I liken it to, in the UK here, we have nine nine nine, but we also have one one one. And you can go on the website and it asks you the same questions you do if you were to call nine nine nine. So at peak, sometimes emergency services get overwhelmed because there are only so many other people on the end of the line. But if you're just answering questions that have yes or no answers, or answers that give the person, oh, my temperature is a hundred and four, when it should be ninety seven, yeah, or ninety six.
20:16 Speaker 06 That then gives them the enough information to say, you need an ambulance, or you need a primary care doctor, or you can just take some aspirin and it'll be fine, right? So imagine instead of having all those humans there doing just that kind of basic work, you have that next tier of humans that can then engage with those things. So scaling that function, offering people advice, getting to the right answer quicker, is something we all look for when we're dealing with organisations. And it can help you get to the human faster, or it can help you get to the right answer faster. So that's another way, and again, like I say, I'm an optimist in this field. But that's just another way of looking at that particular problem or solution.
21:00 Speaker 06 So it's really how you frame it and how you look at it. And AI is going to give you that chance, that's going to give you that freedom to think about those things and put those. Things in action because you're not writing an email that says, oh, we've just launched a new product and it's this. Well, you know, it's this. So just get them to write the email, send it out the door. You don't need 10 people to review it because it's gone through that review process, which has that automated but informed human approach to it, that human in the link we talked about. So send it out the door, concentrate on talking to a customer or solving a particular customer journey that's having some issues with,
21:41 Speaker 06 or understanding the customer by looking at the data, questioning it with the AI, to bring you some answers to questions that you might have. It's transformational and so it's difficult to see where sometimes these technologies will go. Bill Gates famously said, we overestimate what we can do in two years and underestimate what we can do in 10. And that's very much so with AI. But I do believe the marketing organizations right now have a great opportunity to scale, inform, iterate and innovate with AI.
22:14 Speaker 05 It is very interesting because I think you've sort of hit on something there which is quite important, which is if we can take the marketer out of the Salesforce Marketing Cloud and give them more time to think, to explore, to strategize, to talk to customers, to analyze data, the tools should solve on that efficiency equation. I think when it comes to marketing, you can talk, okay, how's it going to help our business grow? How's it going to be a help us become more efficient? I've seen some stats in the past where 60 to 70% of marketers' time is just managing software and data. And taking away a lot of that drudgery should actually give marketers more opportunity to think more bigger picture, more strategically, and then just spend more time with customers. I mean, the more time you can spend with customers, I think, the better overall. So it's a great dovetail into thinking about, okay, well, when it comes to applying AI in the enterprise,
22:59 Speaker 05 there is this interesting story you can create, which is, well, this will help us reduce the amount of time that we're spending manually pulling data, manually creating campaigns, manually writing or creating stuff that is very, very similar a lot of the time. And taking that valuable time for an actual brain to think about the harder questions. So it actually connects with what you said earlier, which is calculators didn't. Makers dumber, it actually made smarter because it took a lot of the drudgery out of computation, out of calculating numbers. In the same way with the words and content creativity and automation, marketing can actually be freed up to work on higher level problems. And wouldn't that be nice to spend less time in sales or marketing client and more time thinking about higher level strategic problems and questions. But we need to wrap up because we've run our course and we
23:43 Speaker 05 could keep talking forever about this topic because there are so many things to unpack here in deploying and developing AI powered apps successfully in enterprise. But a massive thank you to Philip Miller from Progress. He does product marketing there in the AI function at Progress, kind of really thinking through this and one of the most successful companies in this domain on how to actually make AI useful. So that wraps up our second episode of the Martech Podcast. If you would like to get in touch with Philip, you can find his link to his LinkedIn profile in our show notes. You can visit his company website at progress.com as well. But Phil, thank you so much for sharing your insight with us today.
24:21 Speaker 06 Great. Thank you very much for having me. And please do reach out if you want to learn more. And I'd love to come on again. Like we say, this thing changes like the wind. So I'd love to come on again in the future and discuss the latest and greatest in what we're doing in this field as well.
24:33 Speaker 05 Awesome. And we'd love to have you. Thanks again, Phil.
24:36 Speaker 06 Cheers, well.
24:37 Speaker 04 Okay, that wraps up this episode of the Martech Podcast. Thanks to our guest host, Juan Mendoza, the author of the Martech Weekly Newsletter. If you'd like to get in touch with Juan, you can find a link to his LinkedIn profile in our show notes, or you can contact him on Twitter. His handle is Juan Mendoza, but it's spelled crazy pants. It's J U four N M E N D zero Z four, or it's a little easier to just visit his company's website, which is themartechweekly.com. Just one more link in our show notes I'd like to tell you about. If you didn't have a chance to take notes while you were listening to this podcast, head over to martechpod.com where we have summaries of all of our episodes and contact information for our guests. You can also subscribe to our weekly newsletters and you can even send us your topic suggestions or your marketing
25:21 Speaker 04 questions, which we'll answer live on our show. Of course, you can always reach out on social media or handle is martechpod, M A R T E C H P O D, on LinkedIn, Twitter, Instagram and Facebook. Or you can contact me directly. My handle is Ben J. Shap, B E N J S H A P. And if you haven't subscribed yet and you want a daily stream of marketing and technology knowledge in your podcast feed, we're going to publish an episode every day this year. So hit the subscribe button in your podcast app and we'll be back in your feed tomorrow morning. All right, that's it for today. But until next time, my advice is to just focus on keeping your customers happy.
26:20 Speaker 00 Thanks for listening to the MarTech Podcast and I Hear Everything production. Looking to launch or scale a podcast like this one for your brand? Then visit ihereverything.com.
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