Reid Riffs with Parth Patil on Enterprise AI Integration (Part 2 of 3)

0:00Hey, everyone. Aria here. We want to kick off the new year with inspiring conversations about AI as well as practical and tactical guidance around technology. So for the month of January, AI specialist and my dear colleague Parth Patel is joining Reed for Reed Riffs to talk about how everyone from individuals to enterprises to startup founders can harness AI to level up their work, retrofit legacy orgs for the AI era, and build AI native businesses right from the jump. So tune in. You're in very good hands with Parth. And I will be back putting Reid in the hot seat come this February. Thanks so much.

0:41Thanks for the kind word and warm welcome to Possible, Aria. As I talk with Reid this month, I'll be walking through some of my AI projects, demos, and tools on screen. While I'll do my best to describe what I'm looking at for our audio-only listeners, consider switching to the video version of the episode on Spotify or watching on Reid's YouTube channel for the full experience. Thanks. And let's get into it. So let's talk a little bit about how big companies, all of which are talking about doing AI and talking about what their plans are, setting up proof of concepts and doing stuff. Are there any big companies that you've noticed doing AI well?

1:20You know, outside of I think the hyperscalers, I don't, not yet, not yet. I think everyone is expected to have an AI strategy, both like teams, CEOs, board of directors, everyone's kind of pushing AI. And I think a lot of people are talking about AI, but I haven't really seen an AI native company outside of maybe the frontier labs, which are AI native because they built the products. And especially done at scale, because I think these large companies, enterprise, it's very hard to move a large ship. And even when you do see a new technology come online, it has to go through different layers of approval before people can even play with it.

1:58And so experimentation is also slower. So I haven't seen it yet. And also the playbook is still a little unclear. Where do you infuse that? I think that's what we're going to talk about a little bit here. I completely agree. I do have some experience of some of the leaders of some of these companies reach out to me and talk to me. So I know that they're working on it and trying it. But it's still, if you use a baseball analogy, the players haven't even come into the field for the first inning yet, let alone anything else. And so here I am calling it the first inning and you're saying they're not even there yet.

2:27Yeah. Well, they haven't even come out of the dugout. Yeah. Yeah. And so they're talking about coming out of the dugout. They're setting up a committee to study coming out of the dugout. But it's not, you know, it's get on the field. Let's think a little bit about how executives should think about AI. Because obviously one way to start experimenting is personally. You know, start using AI as a chief of staff yourself. But what are some of the things that you think are things that executives should say, look, I need to get my organization. I need to get my people. I need to start learning this.

2:57I need to start figuring out what are, as a company, and our teamwork adaptations are to this. What's the advice you would give to an executive at a call it tech company, but also not tech like ignorant company? If I think about the tools that we now have through language models, especially, I think that's probably where we could narrow our focus. I think, okay, language models inherently can accelerate any language related task. And the biggest language related set of tasks in a company is the communication, the coordination layer across huge teams, and large projects. And so I think about all the meetings that we have, all the documentation we have for those meetings, who's writing that documentation, who's creating the action items, who owns the action items.

3:41This is the layer I think that is very easy to implement AI and get value out of immediately is reducing the friction of coordinating across large teams. I think that's where maybe the advantage of the enterprise comes in. If you have a very AI amplified kind of communication layer and coordination layer where people come into a meeting and maybe you have a meeting kind of like AI that's just also plugged into the company business intelligence that can surface the most relevant things about the problem that you're solving in real time. Or even just like taking notes during the meeting and then deciding, okay, we've agreed we're gonna do this, but did one of us write it down?

4:16But maybe it shouldn't require us to write it down because that kind of effort is no longer something that a human should maybe do. And then who owns that long-term? I think the communication layer where the most initial, the initial obvious value is, because it's mostly text-based tasks. I also think that in engineering and software, the gains in productivity are very, like, it's very obvious that you can get huge gains in engineering productivity. I think it used to take maybe 12 engineers, two years to do like a pretty massive migration type project. And the same group of engineers, if you were to give them quad code, four engineers could do it in six months.

4:49That's the kind of like per person productivity increase and speed increase. So I think what happens is like if the capacity to like solve problems is that much more accelerated, we actually have to look at all the meetings that we have and rethink like maybe like slash a bunch of them and like rethink what the essential meetings look like. And what's the essential core group of people that's going to work on a workflow and like really accelerate that workflow and figuring out what were we previously doing manually that no longer we should be doing and freeing that time up to attack the net new problem space.

5:18But that's kind of like how I think about this. Not surprisingly, completely agree with you on this. I think another way to look at it in terms of thinking about it is say, hey, what if part of what we're doing is a lot of coordination problems? Because it's problem solving, decisioning, and so forth. Those can be used. AI can be helpful and all that. It can be generally deployed individually for that. But on the coordination, what do you do? And so, like, for example, if you record every meeting and you have a transcript, not only do you have a transcript. One of the things that I actually do is not only recording the meetings, but then run it through AI with the kind of prompts of, because, you know, it has a prompt of, you know, here's all the projects, companies I'm involved with, et cetera.

5:56Here's the people that I'm collaborating with in various ways. Obviously, in the company, it doesn't have to include only that. It could also just be everyone in the company. It's like, well, who should I consult with on this? Is there anyone I've not thought about consulting with on this? Who should I notify? What action items have fallen up? And obviously, as you begin to get agentic, you can use your kind of hotkey approach to go, oh, you were talking about this animation project. Should you notify Parth on this? And I could just go, yes. And that's one of the ways where keeping the human loop, you go, well, I'm kind of nervous about it doing something I'm not happy with.

6:34But if you're running it as a kind of an agent that's checking in with you, then all of a sudden our brains don't act like computers. allow the AI to be a computer to remember it's like, oh, this project, yeah, I should talk to Parth about this as a way of doing it. And those are the kinds of things that are building upon your fundamental correct thing, which is the way to start, is every single company on the planet works with communication. They all have meetings. The meetings may need to be reinvented. But by the way, one of the ways you start learning that is start transcribing them and see what happens.

7:06And by the way, then you can begin to break this problem of, well, everyone wants to be in the big meeting because it's like, well, I need to know what's going on. It's like, well, actually, in fact, we can have that meeting with the seven people because other people can then be consulted and informed and all the rest of the things. And what's more, say, for example, you're an executive and you know that that working group is happening. You can say, hey, I want to make sure this question is asked in the meeting. And your agent can then go say, oh, by the way, Reid wanted to make sure this question was at least considered in this meeting.

7:37And, you know, part of my go, well, actually, that's not the right question. This is the right question. Here's why. And as you're talking about it, then A, the whole group does it. But then that also gets back to me. And then all of a sudden, we don't forget things. We're accelerated. We're using these as catalysts for completely changing our gameplay. One of the ways to kind of look at this is there's these decision frameworks and assignments, and whether it's DASI, RACI, et cetera. But now you can think of A as not just who's accountable, but also where the agent is and where the agent's playing.

8:11So one of the challenges that a lot of big companies typically come to try to integrate in new technologies, they set a little group, and they do a proof of concept in a little group, and so they don't actually start experimenting with and just integrate it into their actual workflow process, which is actually, I think, one of the things you have to do with AI. You can't just go, oh, these three people off in the closet are going to do this. So what are some of the things that you would say for big companies to say, look, here are some things to really think about as trying to integrate AI into your company?

8:42What I've noticed about AI, at least in this current form, is it exists at the workflow level. So the workflow is being transformed. Parts of the workflow are text-based, and now language models are taking up some of that stuff that was previously done very manually by people imprecisely. And so if the workflows are being updated, I think actually that's kind of like a bottom-up, like that's a bottom-up thing within an org. Everyone has their own workflows. Every single person knows how their job is done. And when you think about where does the AI fit in, it's the person who does the job is going to realize, oh, wow, we should be doing it in this new way.

9:16And so the gains often come in this bottom-up approach from like I'm working with our translator to work on our podcast translation and I see how how much work goes into like the translation piece and it's like well actually we should focus on the quality of the voice and we should have language models work on more of the translation piece and then maybe like guide the language model so we end up becoming more orchestrators in that workflow and we're focused on where our unique human like our ability to listen to the accent actually ends up being more valuable than the the literal like writing of the transcript so I think that like reimagining the workflow and that being done in a bottom-up manner.

9:52And how you do that is to create an environment that is sharing those wins across. You want to reward that experimentation and celebrate, oh, here's what we learned this week. Here's how much time we save. Here's how many steps we skipped in this process. And now we actually don't need these three meetings now because we have an automated solution here in place. I've seen companies that are more resistant. They're not resistant to AI. They're just not, they're very close-minded about it. And then what happens is that the productivity gains end up, like someone gets really good at doing something, but they don't feel like they can share that.

10:25And they don't, they're like, well, if I share that, then like maybe I'll get in trouble. Like this new approach to solving problem, I just want to do my job faster and no one needs to know. That kind of, Ethan Mollick calls it the secret cyborg. And that's fine on a short-term kind of thing for the individual, but really a team that rewards that experimentation and sharing the ideas, like where you and I are bouncing ideas off of each other, that team is going to go way further. And that those learnings end up becoming like organizational learning, right? So it ends up being a collective kind of a pursuit.

10:55As a funny parallel, what percentage of Hollywood writers do you think are secretly using it at home? And then since they're not allowed to bring it into the writing room, what would your guess be at what percentage it would be? At least 40%. I think it's at least 40%. I was going to go with 70. 70? Yeah. I mean, I bet some. I bet some. And they're very, they won't admit it. Yes. But then when they talk about it, when they do admit it, they're very much like, I don't like that it doesn't have perfect recall on every scene that I committed. And I'm like, well, okay. So clearly there's a, there's, you're using it.

11:24And we need to make it better. And the rappers need to get better. There's going to be an agent for that. That's going to be much more deterministic, but you can see that there is a strong desire to like bring them into the creative process. One of the things we were talking about was in integration, a lot of it has to do with communication, a lot of it has to do with team dynamics, a lot of it has to do with obviously individual amplification, but also workflows. Can you describe a team workflow that wasn't possible pre-agent large language model, but is now tractable? Something that maybe you've built or seen that is new in terms of how to conceptualize what kinds of reinvention of these workflows is possible?

12:04I'll show you, actually. We take a look at my screen. We have a Cloud Code agent. And in this folder, we actually have a bunch of data. We have a bunch of CSV data on order items for a fake toy company. Just a bunch of CSVs. So we're going to say, Cloud, look at every single CSV in this folder, analyze the data, however you see fit, and then build me a dashboard so we can drill into the insights and visually understand what's going on in the data in our company. So I think that data analysis is one of these coding adjacent business intelligence and coding and coding agents are actually very closer together than I ever imagined.

12:40And that came from business intelligence, came from finance, came from data analysis. But then we do the analysis using code. And then when you take a coding agent and you plug it in, you give it access to the data, as we're doing over here, it's looking at order data, refunds, order items. The same thing that I was, it's going to analyze the data with Python, and then it's going to build a dashboard. The kind of thing that I would have done that would have taken me two or three weeks to build this kind of dashboard, I suspect we'll get the first version in under a minute. And this is something that doesn't matter how large or small your company is.

13:08You have data and you may not have enough analysts to slice that data, but now you have this extra cognition we can just aim at the problem. And what used to take three weeks can now be done in a few minutes. So here we have a completely generated dashboard from just six raw csps and so maven fuzzy factory so this is an imaginary toy company that these csps represent and you can see the revenue growth over time profit growth over time conversion analytics rate of conversion over time and it's really like a comprehensive it's a pretty good dashboard for a single prompt of of data analysis and you could you know you could ask a follow-up question i think i'm sure we could ask more the ability to drill down into this data.

13:49But a lot of this kind of an analytical work, business intelligence type of work, I mean, I think of it as like business intelligence is a subset of general intelligence. So we should be able to use AI to do business intelligence. I really think that BI is a subset of AI. This is how I imagine the role has already evolved. It's like you have a folder of data, point the AI at that data and have it make sense of it. You know, I asked for a follow-up question and I asked for it to create like a McKinsey style presentation on top of the data. So let's take a look at that. And this does look like some presentations I've seen from McKinsey.

14:22We've got the classic McKinsey color scheme type. Just about everybody in McKinsey is actually at least experimenting with individual use. So I think this kind of thing is the kind of thing they will see. They will. They will. I mean, the moment I have been looking at, ever since InterEquo and GPT-4, I was like, wow, we don't have to build presentations by hand. And it was surprising to me that the AI will build a web application that looks like a presentation. So this is an HTML file with some CSS and JavaScript. But when you think about code as a general solution, then it's clear that code will be used to reimagine the business intelligence.

14:56And it makes it so that if this can be done in one minute, then we can go much richer and much deeper in our depth. Well, part of the things that I think cause this acceleration is people don't realize how this acceleration completely changes the game. It's a difference in degree, but it also makes a massive difference in kind. And part of that's because an executive can just do this themselves and not like, well, I write an email to the data scientist, the analyst team, and they process it when they get to it in a couple hours and they do the work and they get back to you the next day or the day after, etc.

15:29But the learning and thinking about that as a loop. The other thing is you can now ask a whole bunch of different questions that you hadn't asked before. Or you could do the interview me, the earlier prompt, until you go to get to the right kind of thing. The right artifact that would help you unpack the trends. So all of this stuff becomes possible everywhere within the company. And obviously part of the transformation that's going to be very important in companies is, yes, there will be some rework of the meetings, some rework of the team process, and those are good things to do. But a lot of it's also going to be individuals bringing their stuff in.

16:05And as you mentioned earlier, being able to talk to each other about it and do collective learning. How do we collectively learn this and adapt this better than other organizations as part of it? I mean, we might be doing this in a meeting in real time. We have the raw data, and then you and I are prompting the same AI to unpack the insights in the company. Exactly. And so what are some of the easiest things you think companies should consider automating within the corporate stack? Oh, I think one of the easiest and highest leverage things. So before language models, a big complaint I always got from people who are trying to think about data and data analysis and insights is that we don't have clean data.

16:45Our data is not clean. It's very messy. The interesting thing is that language models are uniquely very good at cleaning up data. You can give them a whole customer complaint and turn it into action items. These are the action items. This is the main takeaway. The three things that we should take away from this in whatever structured format. And then that fits into your CRM. It fits into your traditional business logic, which is more fixed. So I think that eliciting structure from your unstructured, messy world of your business is the obvious first thing with language models. And then the next thing is figuring out the follow-up actions.

17:21Like, what should we do next? So here we have a slide. It says immediate next steps. And it's like, well, we should review the mobile UX because it seems like the conversion is lower. Certain products that are more successful. It looks like certain, the AI is already doing this analysis that would have otherwise taken me like three weeks of just like being in the weeds. It is pretty shocking to me. And I cannot imagine working with an analyst that is not doing this, right? Like every analyst I hire and work with in the future is going to be working at this kind of speed of like question answering and data visualization that I kind of expect.

17:56Yeah, and people, I think, worry there's like, oh, a bunch of work goes away. But actually, in fact, what happens, a whole bunch, this is an example of where a whole bunch of new work gets created. Because if you think, well, we only have been asking the most minimal questions before because it was so expensive to do it. Now it's a question of, oh, well, for example, on these conversion rates, does the conversion rate change by time of day? Does the conversion rate change by holiday? Does the conversion rate change by, like, give me all the variables that it might change by. Okay, well, in addition to that, you know, are there any surprising things that we might learn and conversion rate, like time responsiveness or other kinds of, and you could just be keeping going.

18:35And that's what the task becomes. That's the new job. The new job is asking the AI to do the right thing and figuring out what the right questions to ask are. It's no longer like, did you write the right SQL query? It's not the syntax of like, did we write the right query? But now we're at this, like, we're all kind of at this more macro orchestration level of, I think it's amazing. I think it's a total expansion of the analyst kind of role. We both follow Sam Shalachi, and it's a little bit like his description of, look, there isn't coding anymore. There's system architects that you're using.

19:06And the same thing is, by the way, true of lots and lots. It's a parallel. One of the things that's interesting about all, like one of the things our entire audience should take away from you is even when you're talking about coding, coding is a parallel. It goes, oh, that's coding. That's different. It's like, no, coding is a parallel to data analysis. It's a parallel to creating memos or PowerPoint. Operations. okay it's a parallel to doing legal stuff it's a parallel to doing auditing and risk analysis it's a parallel to all of the stuff about like how is it that you orchestrate for now being able to do a ton more work in a short amount of time greatly expands the kinds of things you can be doing that are value creating so it isn't that your previous thing which took three weeks and now done two minutes like well then i'm going to spend the rest of the three weeks playing halo It's like, no, no, I can actually do a whole lot more and create a whole bunch more value here.

19:56And so the job is still valuable. Yeah, we can fan out. And this is where the computers are very useful. It's like humans, we are single-threaded. We can only really work on one thing at a time. But if you say, let's expand our analysis and look at 10 different angles at the same time, that's the kind of thing that's unlocked when you lean on the parallelization of the computer and the coding agents that can work in parallel. You can spin up many of them and then you can attack a problem with many angles at the same time, which is a totally new capability. Part of the thing that people don't understand is that learning these tools is not learning how not to do your job.

20:31It's how to learn to do the job in a way that you have superpowers. And that's the whole part of super agency. So one of the things naturally is that most people are non-technical. Part of their fear and concern about adopting new technology is that they don't know how to use it. They don't know if something goes wrong. They don't know if something happens. What do you think is the right mindset for kind of the non-technical executive for thinking about this and kind of why to engage? And then what would they need to understand about how the models work? or what is kind of some of their potential experiments for how do they lead well in the AI age?

21:11I think it's less important that you know how a language model works and more important that you know what it's like to work with a language model. Working with the tool is a new skill. Less important than say like, why does it predict the next token a certain way? I think it's more about like, what can this model given access to these tools do for you? and increase i think the technical you know right now the coding agents are the most powerful but eventually we will get their counterparts that are non-technical friendly i think bod and chat gpt and these tools will become even more powerful and more agentic and and for example like they might be organizing your digital life helping you organize all your files your like your health care records your personal life your work life creating that context enriching that context retrieving it when you need it those paradigms i'm already seeing in the coding agents and I'm sure they will they will end up cascading to the non-technical experience if you're ambitious and I think I think more people should be ambitious because you can teach yourself anything today I think and this is something that some of my like non-technical like some of my old bosses they've come to me and they've been like I have time I can learn something what should I learn and in those cases I do push them to play with a cloud code or a codex to get a sense of what it's like to have an AI on your computer working side by side with you organizing your work, creating daily automations.

22:33There's things that you can't do in chat GPT. You go to chat GPT and you ask, generate a hundred images. It's only going to do like three, and then it'll just kind of just stop there. But if you go to a coding agent, it will write a program that can generate a hundred images. And so once you want automation, when you want personal automation, you have to leverage code. It's just that right now those tools are a little bit more for the technical person. I still think it's never been easier than before to get Claude running. And if you've seen anything of how I interact with this, I'm not coding.

23:05I'm talking to a chatbot that writes code for me. So I'll generate thousands of lines of code without having to personally code them. It's mostly like I'm delegating to coding agents on my behalf. I think we will all be doing that eventually in some capacity. So if you're ambitious, pick it up now when it's a little bit like early and you get that head start on it. Otherwise, take the most technical person you know and equip them with and invest in their coding agents. It'd be like, you should be using cloud code. If you're a non-technical leader, empower your technical counterpart, your CTO, to be using these tools and to cascade that throughout the firm.

23:40Because that is like unblocking them, putting them in a place where they're not afraid to learn and experiment and discover the value is the most important thing you could do. I actually have directed a number of people who are non-technical to start vibe coding. and it's still a little rough for the non-technical person. So for an executive, one of the things you can do is actually go get a technical person and say, look, these are the kinds of things I'd like. Set this up for me. Do the vibe coding hack. Sometimes some of the things I ask you is like, look, set up this vibe coding thing for me so I could start using it because then I'm getting that experience and that foresight into what being AI native, making it happen is, and I don't have to learn the current hard edges of vibe coding.