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Want generative AI that actually delivers results? It's harder than it looks. Two leading AI startups that specialize in corporate readiness explore the models and techniques tech leaders must consider to deploy effective AI systems. Open source or closed? Fine-tune your LLM, or retrieval-augmented generation (RAG)? And just how good must your organization's data be? We dig in.

From Rag to Riches

Conferences

Great guys, so we're going to talk a little bit in this session about you know how enterprises can really get AI to work at scale and deployments, and a lot of that has to do the title of the session is from Rag to Riches and rag here is retrieval augmented Generation, which some of you. Mayo is a technique for helping AI get the data it needs and the context it needs to give good answers, but we know, you may not be familiar with my 22 panelists. So Dawa contextual AI, just 30 seconds. What is it? What, what do you guys do as a company? Yeah, so you mentioned rag. Reg is really about making Gen AI work on your data, right? So I think one of the The interesting thing about where we are right now is we have these amazing language models and amazing G AI, but the question is how do you make that work on your data, and the problem to solve there is the context problem. How do you give it the right context? So that's what we're focused on enterprise startup, uh, solving hard problems with context engineering, essentially. Got it. And, and Arvind, similarly, Glen, for those who don't know Glen, what's Glean do? Yeah, well, I mean, think of GLEA like GPD but inside your company. It's a place where your employees go and ask any questions or give it work, and GLEN will use all of the world's knowledge as well as all of the internal company's context to, to actually answer those questions or complete those tasks. And so in many ways, you know, I think, you know, the underlying technology, the concept that we use is similar, which is understand your enterprise, understand your people, your data, your knowledge. And bring that as useful context, ah, combine that with the power of language models to actually solve, you know, real tasks and answer real questions. And you guys have seen like tremendous adoption, right? You've just hit 200 million in, in annual revenue run rate, is that right? Yeah, that's right. So we just a couple of months back, you know, we, you know, announced that milestone, I guess we did today, but the, uh, but that's exciting, you know, business is growing for us, uh, rapidly. Great, um. So Dawa, you sometimes talk about sort of RAG 2.0, that there's this kind of new way of doing retrieval augmented generation. What is that really and how is that? How are people doing rag today that might be different than how people thought about doing it, uh, maybe right after chat GPT came out in, in 2023? Yeah, or even before that, right? So RAg is older than, than chat GPT. So, um, so my claim to fame originates is, is I'm an AI researcher, right? So I was the project lead on the original RAg project at Facebook AI Research. And at the time, the way we did things, vector databases didn't exist. The model we gave the data to was not called a language model. It was just a sort of generative model, but it wasn't a language model. So if you look at where we are now compared to then, it's just a very different idea even. So, so the core framework is still the same. You're still doing retrieval to give context to the generative models so that it can deliver value. Uh, but the way you do that is very different. Um, and that's why when we started contextual, we were really obsessed with this context problem. I still think that's the most exciting, most interesting problem right now. So we have these amazing language models, even if you freeze all technology, and you just say, OK, this is, you know, whatever GPT 4 5.2 or Gemini 3, we just keep that. There's still a decade of innovation that we can do just from focusing on the context. Um, and so the, the way you really want to do that is not just a vector database, right? There's a lot more going on. We're, we're doing agentic rag these days. We're using retrieval as a tool, and there are many more tools that you can use. So that's why we, we were looking for a new. We called it RAG 2.0. I don't know if that was the right name really, but I think the core observation was that what you're building there is a system. So it's not a model, right? The language model is maybe 10, 20%, and so it's the same for something like Glean, right? That's an extremely complicated system. And the language model is just a very small part of that, and I think that's why we need to have something like a rag, right? But then if you want to do that the right way, then you have to start from the systems assumption, and that's what, that's what we've been doing. And if you do that the right way, then you get much, much higher accuracy. So that's really what we're seeing kind of across the board is. Enterprise AI adoption is all about high value use cases and if you really want to get there, then you need high accuracy and that's what we do much better than anybody else. Interesting. And, and Arvin, for getting this to work well, Enterprise search is key. How, how do you do that successfully? And when you, uh, you know, there's a lot of data out there that a lot of enterprise, uh, uses of, of AI are not really scaling. They're not getting full deployment. What do you think the pitfalls are there? Why, why, where are companies going wrong? Yeah, I mean, see, first of all, enterprise environments are very, very complex. Um, there is lots of information, lots of data across hundreds of thousands of different systems. Data is permission, it's governed, like not everybody in the company can access our data. So when you sort of start naively, and you say that like, look, I'm gonna actually make AI work in my enterprise. I'm gonna connect AI with all of my systems, that's when you start to sort of run into. all these challenges. Why AI is so hard. Like, first of all, just connecting all of the enterprise information, like bringing that context into AI is hard because that means you need to build integrations to all these different enterprise systems that you have, understand the data models, understand the APIs, you know, to connect with that data. Then the second challenge you run into is that, well, oh, you know, I, I launched AI and I connected it with my Um, with my documents, but you didn't actually take care of security. And you have now AI having access to really sensitive information inside your company, and everybody in the company can then go and use it. So, so like when you, like, you know, like this, this, you know, we're all in the very early stages of making AI work. There was like this tremendous pressure from the executives, you know, uh, team, you know, within the company. Um, we had to do something, make AI work, and we didn't really, they properly like sort of prepare. For it and how to, how to make it happen correctly for us like at Glean like you know we've been in business for now almost 7 years, so we're just like, you know, like you know you're one of one of the original sort of enterprise AI focused companies. The, the first application that we deliver to our customers is, is search, which is, you know, we go in and connect Glean with all of our enterprise systems and we sort of understand more robustly, first of all, you know, what is all this. Information that you have inside the company, what information is up to date, uh, is high quality, what information has become obsolete at this point, because, you know, when you think about context again, you have to make sure that you're using the right context and making AI work on it as opposed to making AI work on scale. How much of that sort of data cleaning can be automated and how much of that is still kind of a manual process? Someone has to look at a human has to look at it, say, yeah, this is out of date. We're not actually going to put that in the database that we allow the AI system to access. Yeah, I mean, I think like when all these projects failed, like, you know, like if somebody needs to take the blame, so who took the blame, you know, the, uh, the startups, you know, and the product companies will tell you that, hey, your data sucks, you know, it is bad and it's your fault, right? So go and fix it. But the reality is that humans cannot fix, you know, the data. You cannot suddenly change our, our habits. You certainly can't like get people to. You know, actively maintain, manage information, keep it up to date, remove the steal information out. That's not how we work. Like, you know, it's, you know, we're going to work the same way as we work today. And so the, the right solution is for AI itself to figure it out. AI has human capabilities, and if you have the right AI, it will actually you know what information is good and what is not, and it will actually make careful use of, like, you know, you know, the information the right way and again, like, you know, the right security. Do, do you agree with that? Is that you can do that. Yeah, and, I, I think one of the most sort of misunderstood sayings I often hear from people is garbage in, garbage out, which is total BS. I think like what, what you want is garbage in information out, and that's why you want to use AI in the first place, right? So I think that is a very common misconception. So maybe we're not quite there yet, but In the long run, what you want to do is just apply AI to your data and all data in an enterprise almost by necessity is going to be garbage. So the question is how do you get the useful information out of that? Absolutely. I'm going to try to get one question from the audience in a second. So think of a question and I'll hopefully get. At least 11 in the audience, um, there's a lot of talk out Arvin, uh, uh, about sort of context engineering as like the next frontier of how to get Enterprise A out of work. What's your view on that and, and how are you, how is Glean working with things like MCP, you know, and scripts and all of these new ideas about giving more context to models? Yeah, I mean, I, I think that that's exactly like the new frontier of innovation is like for any given task, like how do you. build the right context and context is dynamic. Like it's not that like, hey, I have this task, and you suddenly just like you go into all these systems and pull some right pieces of information and you're done. It's actually iterative process where, you know, models and agents are going to be solving complex tasks and Every step of that execution, you have to sort of like figure out like what's the best way to bring the best information to these agents so that they can do their work properly. Um, so, so that's, that's, that's absolutely the key. Like, you know, what we do at GLEAN is, we are, I mean, we think of ourselves as we're trying to build, you know, your enterprise brain. Um, the idea behind GLEAN is you connect Glean with all of your different systems inside the company. So now we understand. You know who your different people are, um, what are the different business processes, projects, teams, uh, product lines that you offer, so sort of building a deeper understanding of your enterprise and also work like, you know, what are the different business processes and how do individuals go and actually execute those business processes today without AI, like, you know, what are humans doing? So sort of if you observe, if you actually connect with all the systems and you observe all the human activity, you're able to sort of then bring, you know, that collective human intelligence. of enterprise in one place and you do that and this is basically the repository of context and now from here, like when you actually ask AI to do some, you know, some piece of work you've got to sort of now, you know, from that repository pick, you know, the right context, you know, and that right context really is like, you know, well how did humans in the past did similar things, you know, in this company so that AI can sort of use that as the as the baseline to start and then do do its work and that's how it sort of really improve accuracy and. And and build these agents that are uh that are capable of actually extending, you know, that human intelligence and, and, and, and automate work. Alright, I wanna get, I wanna get one question for the audience. Who's got a question? And if no one does, I've got, oh, right over here is a guy, uh, with a question. Please state your name and where you're from. Rick Chavie with VX, uh, enterprise scaling platform. One question we, we are getting with our customers is around. If they have a lot of M&A and they've got multiple ERPs, e-commerce, CRMs, whatever, is coming up with that consistent framework unified to be able to apply the agents or any other technology for that matter to give that sort of single view of, of revenue execution or or whatever application you're looking at. So I'm, I'm wondering how. With your models, you know, how you sort through all that confusion, or in some cases systems may not even be really automated. You have to, yeah, and it's, do you need a single source of truth, or can you work across multiple systems? You should work across multiple systems again, so I, I think in a, in the complexity of an enterprise you are not going to have everything in one place. Right, so that's why we have things like MCP, some of the information you might want to index so that you can do faster retrieval over it, but, but in the end I think you just have to accept that you are dealing with a sort of messy reality of an enterprise and now you need to have an AI system that can deal with that. So part of that is understanding what you need to look for and so you know, doing the right tool calls, hooking up into the right ERP systems. A lot of the complexity that that that. that you solve with something like a glean or also with our platform is really about hooking up all this data. Now you have this, so you have this kind of data layer then you have The intelligence layer and then the missing part is the context layer. Like how do you get all of the right little bits of context from that in an iterative way so that intelligence can be useful. Arvin, I, I want to play something you said. You talked about having the AI learn the existing context and the existing workflows to sort of replicate those, which is probably a good place to start. But there's this tension there because a lot of people want what they really want to do with AI is reinvent workflows and change workflows. Can you do both at the same time? How, how does that work? Yeah, in fact, actually what I was saying was, see, there is that human intelligence, the human way of doing things today in your enterprise, and that's the, that's the starting point. So when you, when you, when you ask AI to actually solve a new problem, like either, you know, you let it loose, you know, with no sort of, uh, baggage, you know, to deal with, so to speak, like, you know, like let AI actually go and figure out. From, from scratch, like how to solve that task, or you say that, well, look, you know, like, you know, there's, there's some value in you knowing, like, you know, how we've been doing this work in the past. And you actually bring that context in and then, but you don't like, you know, you don't ask AI to sort of replicate that exactly what the, you know, what, how you were doing that business process before that actually just serves as the inspiration, the starting point, and AI can actually make its own decisions, you know, on top of that. So, that's how it works. So like, you know. Most of the use cases like as people are building these agents today, what we're seeing is that AI converges faster and is able to add value and you know, sort of like, you know, conforming to like you know how the business really wants to work if you bring in that, you know, context. Interesting, um, I wanna do a couple quick rapid fire rounds because we're, we're running a little low on time. Um, if what do you think is the most overhyped sort of, uh, AI technology at the at the moment? uh, what, what do you have to say? Agents, agents, Arvin. I, I, I, I, I would say that any technology, you know, any cool demo, like, you know, it seems to make a complex business process somehow like, you know, magical and automated right away because none of them is gonna work. Interesting, but I think on agents, so I think agents. Is most overhyped but also probably most underhyped. It depends on the time scale you're looking at. Interesting. I was, well, that's my next question is, you know, in, in the next year, what do you think's gonna come along? What, what, what, what's on the roadmap that you think's gonna make the biggest difference, to kind of enterprise implementation, Ivan, what do you think? Well, I mean, I, I think the models like are delivering amazing capabilities every year. Ah, this year was a big one for, for us in terms of the, you know, the, the Like the level of agents, the complexity of tasks we can handle through the thinking models. So I actually don't know what's coming next year, but again, like, you know, to, to what you said before, I think we're very happy with the underlying model technology. And, and now it's time for us to sort of really build robust integrations into enterprise systems and building a deeper understanding of. Um, your enterprise processes and automate them, but for me, like what, what, what's most exciting is that next year AI is going to become proactive. Today, like most of the AI use inside the enterprise is reactive in the sense that I, as an individual, have to go to AI, make it, ask it to do something for me, and it does it, and very few people do that. People don't have time to actually make it happen. So next year I think we're gonna see the, see this flip and AI. OK. Do, what do you, what do you see coming? Yeah, I, I think test time compute to your point is, is really, I think a lot of people are underestimating what this means. So this is really going to change everything. We are just getting started, um, and so when you hook up all your data and then you have these smart reasoning models, uh, that can do so much more, we are going to unlock all of these very complex use cases that really seemed like science. Fiction and that's already happening everywhere now. I mean, like even with things like coding, right, but how good we are at that now with AI. I, I don't think I would have expected that even a year ago. So, uh, it's going to come very quickly. I, I think next year is really going to be the year of, uh, of ROIs and through complex. Well, we're, we're out of time. We'll have to invite you guys back next year to see if your predictions are right. Thank you very much for, uh, for joining us. Thank you. Thank you.

"Move fast and break things" has long been a Silicon Valley mantra, but the rise of AI has us rethinking every component of the modern corporation. What's mission-critical and what's not? Can you be too lean and mean? And: In an Intelligent Age, is killing organizational complexity theultimate competitive differentiator? Two Valley veterans discuss.

Death to Complexity

Adding AI to the enterprise can feel like reassembling a plane mid-flight. How should corporate chiefs strike a balance between reshaping their business for an intelligent era with maintaining the quality of operations that their customers and investors demand? And what parts are worth keeping proprietary? AI leaders from a trio of Fortune 500 companies discuss.

How to Rebuild Big Business for AI

What do the world’s biggest companies get right—and wrong—about AI? Corporate AI whisperer Allie K. Miller shares the most urgent insights from her work consulting across industries, revealing how enterprises and individuals should think about scaling AI responsibly, sustainably, and competitively.

The Fortune 500 AI Forecast

Good morning. And amazing to see so many clients out there. Have I told you that you look beautiful today? We are going to be talking about change management, and I want to kind of start it off with a little bit of a hot take. I want to stir the pot. If we could get the slides on, we will be able to kick it off just yet. The hot take that I want to share with you all. Is that AI is not a tool, and I actually think that calling it a tool ends up being a little bit of borderline self-limiting behavior that is holding enterprise all around the world behind and that they're actually stuck. Uh, AI is a weird thing. AI is a little bit closer to like an alien or a beast, and it is actually leaning into that weirdness where leaders can gain an unfair advantage. So why is it weird? Unlike traditional software software 1.0, where you would put in one input, you'd get the exact same output done. AI allows you to reason, adapt and respond. Now that method of working opens up. Four ways of working with it. What are those 4 ways? What are those 4 interaction modes? The first is AI as a microtasker, AI as a companion, AI as a delegate, and the big one AI as a teammate, and I want to show you what each of these looks like. The first is AI as a microtasker. Every single one of you has used it this way, at least where we're asking trivia questions like which came first, Fortune magazine or the FIFA World Cup. We can give it a perfect 18 page prompt for the greatest M&A dashboard of all time. And we can ask it for songs and audio and video and infographics. We get back that trivia question. Fortune magazine came first in case anyone was curious. We get our beautiful dashboard that would make me cry with joy, and 90% of your employees are stuck in this mode, and so many employees think that they are an AI super user when all they are doing is asking AI to write their mean email in a slightly more polite way. And this is holding companies back and actually your annual subscriptions are made worthless because people are stuck in this mode. So let's talk about the others. AI as a companion which Joe talked about is the ability. To work real time with AI side by side where it codes with you, edits with you, talks live with you. I'm kind of obsessed with this one, sees what you see, and this is where AI becomes a lot less transactional, where I am assigning something and a lot more relational that real-time collaborative work. And some people have taken the term companion a little bit too far. Uh, I'm not advocating for work husbands, work wives that are digital, uh, but we'll save that one for like a new TLC show or something. The third interaction mode is AI as a delegate. This is when you are assigning large tasks, maybe they're 40 minutes long, and you're then getting it back. We can see things like inbox management. I know people who have fully offloaded their inbox to AI. They spend 1 cent a month and they don't even touch their own inbox. There are people managing workflows and of course very complex multi-step AI. This is me using an agentic browser and asking Chat CBT to build me an entire AI avatar video with me not even making a single click. This is really where job transformation is moving into real task reallocation, and most companies have not even gotten to this stage. We're going to talk about one more though. That is AI as a teammate. Now, I started in AI almost 20 years ago. And this is one of the most underrepresented, underutilized, and forgotten modes because it kind of only started about a year ago because of AI's performance. Two examples engineers from OpenAI are treating codecs inside of Slack as a teammate, pulling it into trap threads, working all together, and second, maybe AI is sitting in on your meetings, fielding questions, taking action. Now the big difference for AI as a teammate is that AI is lifting up a system or a group and not the individual. This is where AI is helping your entire engineering team or every single person in that meeting and lifting up the system. That is the big difference here. What are you optimizing for? And this is AI as operating infrastructure. We are going to have AI agents and systems that are sitting where we work slack Zoom Teams, what have you and they will be ambient they will be gathering information and importantly we will no longer be prompting AI. I don't know if people have quite come around to this AI will be prompting us because it will be in our systems and helping our team as a whole. So all of this, how do you actually operationalize it and what do I wish more leaders were working on? That is a brand new mental model that every single leader has to curate at a company level. You guys have heard of MVP, minimum viable product. I'm not gonna have you raise your hands, but I assume it's 100% because we're an SS. We are gonna take that methodology and we're gonna apply it to the world of AI agents. Because the capability that everyone has to now build out is carving out safe, minimum viable autonomy. What is that small safe bet that you can work on company wide, and I'm gonna break out how to carve out space for this. I have about, you know, 2 minutes to solve all your world's problems, but we're gonna talk about how you can get it not just with your engineering department but all departments, and I'll give you a template that you can steal as well. So this is kind of the core checklist for MVA or minimum viable autonomy. You are going to scope it tight. You are going to keep a human in the loop. Maybe it's just as a final check. You're going to keep it modular so that as AI changes every week or hour or minute that you can adapt with it. You're going to know your risk thresholds and we're gonna talk about that in a bit. And most importantly is logging and measuring everything and figuring out if AI is good or not, and that last one matters more than you think. Why? Because evals, figuring out whether AI is good or not is the new product requirement. We are no longer giving step by step perfect instructions to these AI systems. We are going to provide goals and boundaries and rules, and AI systems are gonna work from the goal backwards. They are going to check their work against our goals and so instead of defining these perfect 18 page prompts, we are instead giving it the sphere that it is going to work within. This is goal oriented AI. This is a fundamentally different kind of software than we are dealing with today. The capability that everyone should be working toward now until we have AI built into the hardware in our brains, which I'm sure is going to come like tomorrow or something, is agent protocols, and I want to give you some examples. Clawed skills is the ability to teach AI how your org operates in one way and it remembers it forever. I built out a. skills that uses an AI first index assessment. This is over 700 lines of directions, code, templates, resources, examples. I've trained this clawed skill, and I can now run an entire assessment on how AI first a company is in minutes. And this is something that used to take me weeks. Agents. MD, which is much more in the coding space, is from OpenAI. This has been used on over 20,000 GitHub projects, and what I love, one of their best practices as they're giving AI tasks, they group them into always do, please ask first and never do, and I love AI agents with healthy boundaries. My therapist would be so happy. So one template to steal, this is how I'm bringing it to not just engineers. This is a preview of something that I share with clients and as you're going through this template, realize that it actually takes hours or maybe even days to fill this out. This is not a quick one off prompt. This is architecting AI teammates throughout your company. I want you to be thinking about how to document the human process today, how to measure it. What the compliance requirements are that your general counsel becomes your best friend. I want you to be thinking about, uh, checkpoints for AI and what responsibility you can give these AI agents. I know companies that allow AI agents to spend up to $1000 without anyone checking it. I know companies that have an even higher spend limit for AI agents. OK, not only think about that, but when to escalate to a human. This is how you have safer and responsible AI use. And also, until we have self-learning AI, how are we optimizing these AI systems? Now I mentioned risk thresholds before. Importantly, as you're building out your agent protocols across your company and leaning into MVA. I want you to take about 70% of them on low risk tasks, about 20% on complex cross-department tasks, and about 10% on strategic tasks that change the structure of how you work. 70 2010 and this template can be used across all of them. Now, I'm gonna give you some final predictions here. These are 3 bets that I'm making and happy to argue with anyone during lunch as I'm eating some sort of sandwich. OK, 3 predictions. Number 1 is that AI will likely continue to get better. And what would I do about that? I would lean into MBA, build out that autonomy portfolio, and figure out how to move out of the polite email prison and move into AI as a delegate and AI as a teammate and lean into that weirdness. The second prediction is likely in the next several months AI will be able to work autonomously for over 8 hours uninterrupted. In order to take advantage of this, knowing that it's likely coming, I would pull together your employees and start thinking about use cases that take advantage of this and running some sort of MBA hackathon or agent hackathon and require that through that process they write out an agent protocol. You can even come up with your own template. The coming out of this, you are not only going to have an employee workforce that is trained on AI agents that knows how to use your tools and that is excited to be able to work on this, you are also going to walk away with a perfect stack of agent protocols for everything in your business, and that can be used to train new people, to use brand new AI systems immediately without having that start friction. The third is that the cost will likely continue to drop on today's level models as you are thinking about how to take advantage of what is to come, where are you using one query today? Where in the future maybe you instead use 1000 queries or 100,000 queries, and what would that change about businesses today? Maybe you're gonna build out hyper relevance, hyper personalization machines. Maybe you're gonna build out mass simulations for every market launch you do in the future. These are the three changes that I am taking a bet on. Now, of course, we don't exactly know what the future of AI will bring. We don't know how fast it will come, and we don't know what will happen when it does. But the bet that I am taking is that AI is not just a tool. And the organizations who continue to treat it like one are going to wonder over the next decade what happened. Thank you.

CJ Corp.

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