When Glean CEO Arvind Jain thinks about the future of work, he doesn’t picture a chatbot waiting for you to type a question. He imagines a digital companion that knows your goals, your meetings, even the documents you’re writing, and steps in to help before you ask.
That vision led him to found Glean in 2019 after more than a decade at Google and a successful run cofounding the data management startup Rubrik. Glean began as a search tool for company data. Over time it has grown alongside AI itself, moving from simple search to synthesizing information, reasoning through tasks, and into what Jain calls “agentic AI,” where systems take initiative. With a valuation of $7.2 billion (and our number one Most Innovative Company in Applied AI this year), Glean is positioning itself at the center of enterprise productivity—and offering a preview of how agentic AI may transform the way entire workforces operate.
Fast Company spoke with Jain about Glean’s origin story, the rise of agentic AI, and how enterprises are rethinking both human and machine roles in the workplace. The conversation has been edited for length and clarity.
Before Glean, you founded another startup, Rubrik, where you noticed challenges with knowledge and documents. Can you share that story?
I used to work at Google as a search engineer—that’s my primary training. I was there for over a decade, and then in early 2014 I started a company with a few others called Rubrik, an enterprise data protection and security company. We grew fast—over 2,000 people in four years—but eventually hit a wall in productivity. Per person, we were writing less than half as much code and selling half as much product. People were struggling, and as a founder I wanted to know why. Startups thrive on productivity, so this was an existential issue.
We did surveys, which showed people saying, “Hey, look, I cannot find anything in this company. I don’t know where to go for the information I need, or who to ask for help.” That mirrored my own experience; our knowledge was scattered across 300 systems, and even I often couldn’t find what I needed without my EA. When complaints kept coming in, I thought we could just buy a search product to connect everything, but nothing existed. There was no enterprise search tool that unified all SaaS systems. That’s what led to the creation of Glean. I’ve built search products my whole life, and I felt we could finally build one that would help every employee in every company.
How did that sort of kernel lead you to agentic AI as a solution?
Well, we started the company in early 2019. The word “agentic” hadn’t yet come into play. AI had been a big domain in computer science for a while, but even transformers—the core technology behind today’s language models—were still niche in search teams. Initially, this technology was built at Google to make search better: to understand the world’s knowledge at a higher conceptual level and grasp people’s questions more deeply, matching content semantically rather than by keyword.
We were already seeing big results with transformers in search. So when we started Glean, we knew we could use this technology to build a search product that could handle natural language, understand content conceptually, and help people find the right things with precision. AI played a big role in our first version of the product. In fact, that made us the first enterprise gen AI company in the world, though little did we know it would soon take over.
Over the years, the capabilities of AI models just kept increasing. The first development was understanding knowledge conceptually. The second was the ability to write: to make their own responses and answers. That was super helpful because it let us evolve our product from looking like Google inside your work life to looking more like ChatGPT inside your company. Instead of forcing the human to read everything, we let AI read and return precise answers.
The third major shift was reasoning: the ability to think more like humans. That unleashed the wave of agentic AI. Now you can come into Glean, ask it to do work for you, and it’s connected with all your company’s data as well as the world’s knowledge. It understands tasks, goes into that agent loop, and completes complex work. As models improved, we’ve been able to keep layering those capabilities into our platform
You’ve said fundraising wasn’t a necessity for Glean. Why is that?
I think AI companies are having a good time. In some ways it’s easy to raise capital, easy to do a lot of investment and build your products. For us, we’re a little different in terms of company history. We’re actually the first gen AI company in the enterprise, before it became super hot. So we did need to raise that first round of capital, but we didn’t know we’d be able to raise more unless we generated success, unless we built a profitable product. So we grew up more traditionally, building a high-margin, profitable business that pays.
Over the years, we’ve built a good business, adding value for enterprise customers and getting paid for it. Our burn hasn’t been super high. Later rounds weren’t motivated by a need to invest in 10 areas, but more by opportunistic timing when great investors came inbound. Ultimately, I think it’s a good idea—everyone else is raising capital, and we can’t be seen as behind. It gives us flexibility to invest when needed. But if I look at our balance sheet right now, we haven’t even tapped the last few rounds of funding we raised.
Even so, you closed a Series F this year at a $7.2 billion valuation. How does that affect your ambitions?
As a company we never had a goal like, “Hey, this is the year we need to go IPO or become public.” In my previous startup, we had those goals, and they were irrelevant to the actual business. We had a five-year plan to go public, but it took 10, and it didn’t impact the day-to-day. When we started Glean, I never thought about that as the milestone. Instead, it’s about how do you keep adding value to customers and build a business that grows steadily every year. The latest funding round really serves to make it clear to the world’s largest enterprises—our typical customers—that we’re here to stay. AI is becoming more and more strategic, and every large enterprise knows they have to transform with AI. They want strong partners who will be around.
You focus exclusively on enterprise customers. What’s the pitch to them over competitors?
I think we should first understand what Glean is. Glean is actually two different things. One is a general-purpose conversational AI assistant. Think of it as ChatGPT, but inside a company. We leverage all the best models out there, whether it’s GPT, Claude, Gemini, Grok, or others, and bring them to enterprise customers. Then, when Glean is deployed, it connects with all your enterprise systems and builds a deep understanding of how your business works: Who the people are, the key projects, who’s an expert on what, what knowledge is fresh, what’s stale. From there, Glean Assistant becomes a go-to tool that uses both internal context and the world’s knowledge to complete tasks for anyone in the company, from HR to legal to engineering
The second part is the platform. To build the assistant, we had to create deep connectivity into enterprise systems, respecting governance and permissions. You can’t just train models on enterprise knowledge and make it accessible to everyone; some information is restricted. So we built a platform to power that secure experience, and many customers now use the platform directly to build their own agents and AI applications. We provide the data connectivity and retrieval technologies to make that possible.
So why work with us instead of Microsoft, OpenAI, Google, or any of the other big players now competing in this space? First, we’re pioneers—we started six and a half years ago, more than four years before anyone else, and have built the most advanced technology stack. Second, we’re model agnostic, giving customers access to the best innovation across providers. Third, AI models alone know nothing about your business—you need enterprise context, and that’s what we specialize in. And lastly, large enterprises demand strict compliance and data residency requirements. Meeting those needs has been our focus from day one. This is where we really distinguish ourselves from most AI companies.
What is the role of humans in this agentic AI-filled future?
We tend to overthink what AI means to the world. Is AI going to take over the world? Largely I would just think of AI as yet another technology—one of the most powerful we’ve seen in our lifetimes, but still just a tool in our toolbox as humans. The role of humans is to leverage it, to get the best out of it, to innovate more, work faster, do things we couldn’t do before. From that perspective, what we tell enterprise customers is that the role of humans, number one, is to learn and get familiar with AI and its power.
Glean Assistant is a good way to make that happen because it feels like ChatGPT or Google, so there’s not much to learn. We’re also proactive: Glean is designed to understand you and your work life, and when we detect you’re about to do something, we can say, “Hey, I can do it for you. Here’s my work. You can review it and see if you like it.”
That’s the first part of the human role: becoming the master of AI, learning its power, and leveraging it. The next part is changing how you work with AI. You’re still in control, but enterprises must adapt to get the best out of it. You have to digitize more of your enterprise, capture human intelligence so AI can learn from it. A lot of human judgment today isn’t recorded, but it needs to be if AI is going to help effectively. At Glean, we try to help companies both capture that intelligence and leverage it for future work.
Ultimately, AI will become more proactive. Today, if you use ChatGPT or Glean, you usually have to go ask it to do something, and that’s where you lose leverage; people rarely change habits. Our vision is for Glean to be a personal companion at work, understanding everything about your work life: your career ambitions, annual goals, weekly priorities, daily meetings, what you’re writing and reading. With that knowledge, it can proactively help before you even ask. When AI comes to you, instead of you going to AI, that’s when it becomes truly democratized and everyone benefits.
How do you balance massive data input with security and governance concerns?
First you have to understand that information in a company is fundamentally governed. You as an individual have access to some information and not others. Any AI products or solutions have to respect that. That’s a key part of our architecture: we understand your information architecture and data governance, we know what roles people play and what information they can use. So when we do something for an individual with AI, we restrict ourselves to only the information that person is authorized to access. We focus a lot on delivering safe AI experiences.
And in this future where your personal AI companion knows so much about you—even listening to a watercooler conversation with the goal of helping—you could say there’s a loss of privacy. But you should not lose privacy. In that world, there must be a level of privilege between you and your companion, like attorney-client or doctor-patient privilege, where nobody else sees what you and your assistant talk about. You have to straddle that balance, getting the best from the technology while still fully preserving privacy.
When you envision these agents interacting with each other across different platforms and companies, how do you see that taking shape? Is it an open ecosystem, or a more fractured one?
If you think about software, it used to be very heterogeneous and boxed; different pieces wouldn’t work with each other. That was the enterprise software world two decades ago. Then SaaS came and made things more interoperable. Many SaaS products could work with each other, but it was still hard because they all had custom APIs, and you had to do a lot of work to integrate them even though the systems were technically open. With AI agents, we’re starting from a better position. From day one, everybody knows agents have to be interoperable. Customers are demanding it, and any smart customer today won’t invest in a platform that locks them down because more and more of their business is running on these agents.
With this technology, dependence on AI providers is very high, so everything you do with one vendor has to transfer easily to another. There’s great demand for interoperability, and that’s what we’re seeing. We already have integrations with other agent platforms—you can build an agent in AWS Bedrock, Google Vertex, Flow, or Copilot Studio and still call into agents or tools built in Glean, and vice versa. So we’re really seeing strong interoperability.
Tell me about the UX. How does agentic AI change design?
One thing that has happened not just in our product, but in any SaaS product over the last 15 years, is they all get more and more complicated. You build features because customers ask for them, and now you have a product that can do a thousand things. And 99% of your users don’t even know about them. There’s a constant struggle from a design perspective: You invest in building features but can’t make them discoverable.
So when we built our conversational assistant, we realized design has to focus on the moment: no menus, no hidden tabs. You have to understand what the user is trying to do, decide which capability makes sense, and surface it wherever their attention is. That’s been a key learning, especially with AI products whose capabilities are unbounded. There’s no way to organize them in a menu. Design is the key to getting the most out of AI, and these products will look and feel very different from the last two decades
Looking ahead, what’s the future for Glean?
Our ambition is to be one of the most important enterprise AI companies in the world. We want to be the primary interface through which AI adds value to every person in any company.
One of the limitations of AI today is that it’s reactive, it doesn’t come to you. Glean Assistant is great at helping with tasks, but you have to go to it, and that significantly limits impact. Many people don’t instinctively turn to AI; they’re creatures of habit who keep working the way they always have. So one of our key directions this year is making Glean more proactive: truly understanding individuals at a personal level in their work life, goals, and tasks, and becoming a companion that’s bidirectional. Yes, you can go to your Glean companion to get things done, but it also nudges you and proactively helps with what you want to do.
And that’s what will truly democratize the impact of AI, because now you’re not waiting for humans to be the initiators.
