TLH Ep.51 Rethinking Time in Professional Services: How AI Is Transforming Knowledge Work with Kourosh Zamani

Hello, Legal Helm listeners.

Today’s episode features a conversation at the intersection of artificial intelligence, professional services, and the future of knowledge work.

For this episode, we’re pleased to welcome Kourosh “KZ” Zamani, co-founder of Laurel, an AI platform helping professional services firms rethink one of the most fundamental aspects of their business: time itself. Laurel applies AI to automatically capture and understand work activity across devices and applications, helping firms improve profitability, client delivery, and operational insight.

Prior to founding Laurel, Kourosh worked in investment management and strategic business development, where he developed a strong focus on operational efficiency, business growth, and the evolving dynamics of professional services.

In this episode, we explore how AI is reshaping knowledge work, from how work gets done to how it is measured, valued, and monetized, and what these changes mean for the future of professional services organizations.

Stay tuned for a thoughtful and insightful conversation on innovation, AI adoption, and the evolving relationship between technology and professional work.

Your Host

Bim Dave is Helm360’s CEO. With 20+ years in the legal industry, his keen understanding of how law firms and lawyers use technology has propelled Helm360 to the industry’s forefront. A technical expert with a penchant for developing solutions that improve business systems and user experience, Bim has a knack for bringing high quality IT architects and developers together to create innovative, useable solutions to the legal arena.

Today’s Guest

Kourosh Zamanizadeh is Co-Founder of Laurel, a venture-backed technology company applying AI to how professional services firms capture and understand time. Laurel’s platform automatically captures work activity across devices and applications, reducing reliance on manual time entry while giving firms a clearer view into how time drives revenue, profitability, and client outcomes. The company serves leading law, accounting, and consulting firms globally and has raised over $150 million in funding.

Transcript

Bim Dave: Hello everyone and welcome to “The Legal Helm.” Today’s guest is Kourosh, also known as KZ, co-founder of Laurel, an AI platform helping professional services firms rethink one of the most fundamental aspects of their business: time itself. Laurel is applying AI to automatically capture and understand work activity across devices and applications, helping firms improve profitability, client delivery, and operational insight. Prior to founding Laurel, Kourosh worked in investment management and strategic business development, and today he spends much of his time thinking about how AI will reshape knowledge work, from how work gets done to how it’s measured, valued, and monetized. Kourosh, it’s really great to have you on the show. Welcome.

Kourosh Zamani: Thanks for having me, Bim.

Bim Dave: So before we get into, um, what Laurel is all about, I would love to hear a little bit about your journey in terms of where did it all start and how did you, um, get to where you are today in terms of co-founding Laurel?

Transcript

Kourosh Zamani: Thanks for having me, Bim.

Kourosh Zamani: Yeah, of course. Yeah, it’s a fun story. Sometimes life takes you in, in interesting directions. But, uh, I always had the entrepreneurial bug, um, my entire life. It’s something that I was interested in, but never quite knew how that would play out. And I was fortunate enough that some of my friends that I was closest to from college started companies, built tech, um, and really paved the path for the rest of us.

And sometime in our late, mid to late 20s, um, a couple of my very close friends decided that it was time for them to take the journey as well. And so we teamed up and, uh, we actually started a different company that eventually pivoted into what Laurel is today. And, uh, that was sometime around, I think, 2018 when we raised our first seed fund, a little bit ahead of our time.

Bim Dave: Amazing. And tell, tell me a little bit about your background. Like what, what did you study originally? Like what was the, what was the original plan?

Kourosh Zamani: Yeah, original plan. Well, Mom and Dad wanted me to be a doctor, um, as, as you would have it. I think I got into UC Berkeley as a bio major, and within my first semester, uh, made the switch to business. Uh, and then from there, went on and got my MBA and cut my teeth on the financial services industry. Spent about seven or eight years doing that.

Very much like learning operations, and it was an interesting time at, at that point where I was watching an industry that w- even today is, like, very much human-led. It’s very much relationship based. But it was at the same time that you started to see the first versions of fintech take off. And it was fascinating as a recent college grad seeing, you know, people that had spent maybe their entire careers in that line of business worry about what, what would AI mean for trading stocks?

What would it mean for rebalancing portfolios? What would it mean for, uh, financial planning and management? And to me, I found that really exciting. And I started asking myself, “Well, how, how would this impact other industries?” Which is what gave me a lot of the confidence to look at, at legal tech at the time, uh, which had not been touched as much as fintech had been.

Bim Dave: Why was time the, the problem that was worth solving for you?

Kourosh Zamani: Yeah. It, it, uh, it really started with this concept. You, you’ll hear this a lot in, like, entrepreneurial classes, which is if you’re gonna build a company, don’t focus on, uh, vitamins, you know, build a, a painkiller.

Like, you’ll hear that a lot. And at the time I, I remember we went and, uh, surveyed like 100 different lawyers, and my co-founders were both attorneys.

And so, uh, they had experienced the pains of time. They were both really great lawyers, right? Uh, hated timekeeping. And we went out and just started talking to lawyers about what do you hate most about your job? And time and time again, it was the answer that kept coming up, uh, which made us key in on it.

And at the same time, we asked ourselves like, it’s really crazy that even in this day and age, you’re having a human tell a machine essentially what they did on the machine. And if you also consider the fact that it turns out that machines can track time better than humans can, it seemed like a very obvious problem to solve.

What I think we underestimated is the complexity of solving the problem, um, the limitations of technology at the time to solve the problem, and a lot of learnings along the way and a lot of advancements that have now gotten the company to a point where the problem is solved and we can start looking at the next horizon.

Bim Dave: Hmm. Yeah, no, in-indeed. so so I, I guess there’s– W-when we think about time and we think about time capture, well, it’s– I guess it started with time entry through to time capture over the years. What w- what were the things that you saw that were, like, kind of fundamentally flawed in the way that it was happening today at law firm that then allowed you to kind of really redesign how that’s gonna look for the future in your platform?

Kourosh Zamani: Yeah. I mean, for the listeners that are maybe a little bit newer to the way that this works is m- many, many knowledge workers in professional services, uh, they have to work and then they have this, like, other second job, which is to then recount everything that they did throughout the day, the week, the month.

Uh, and they’re painfully doing this day in and day out to hit targets that can range from 16, 1700, you know, hours a year to 21, 2200 hours a year. Um, it’s quite demoralizing. It’s really tough work. So we asked ourselves, like, what are all the different ways that people are doing this? And y- we got crazy answers at the time.

It was anything from using graph paper to check boxes, to Post-its, to whiteboards, to using Excel, um- To literally going in on Sundays, this is the most common one, and spending a half day looking at every email that you sent, every document that you touched, every URL that was in your cache folder, and trying to reconstruct your week.

And we heard this too mu- too often. Uh, I think the concept of capture was not novel, but it was broken. And what we found is that if a technology is only able to pick up, say, two-thirds of the digital activity that’s happening, then that cognitive load that’s on the human, that trust that you need to build, uh, it’s not ever actually resolved.

Uh, if, if someone told you that, you know, two-thirds of the time you’ll get credit for the hard work that you’re doing, you’ll probably still track your time manually because you won’t ever be able to quite, you know, let go. And so for us, what we knew would be the most important foundational piece of the company would be to build observability that was durable, reliable, and comprehensive, uh, that covered the entire, we call it the digital footprint of a knowledge worker.

And so that was the key area that we focused most of our R&D efforts on and, and enabled not only better activity capture, time capture, uh, but really a better, um, methodology of capturing data, which is really what, what our company is about. We’re not a, a timesheet company, we’re a data company.

Bim Dave: Yeah, no, it makes sense. W- was there anything that surprised you when you were kind of analyzing some of those work patterns from the data that you, that you see?

Kourosh Zamani: Yeah. The, the biggest thing is like, you know, the amount that, um, a knowledge worker jumps around among different… We’re seeing anywhere from two to 400 distinct activities on a computer, uh, in an eight-hour workday. And so, uh, it’s… Work is quite fragmented. It’s distracted. Uh, 40% of people are checking their emails before 6:00 a.m. and forgetting that they, that they did that. Uh, they’re checking their phones several hundred times a day. Uh, they’re bouncing back and forth between two to three monitors. And the amount of tooling that we are getting, the amount of alerts, notifications, like it’s grown exponentially. And so the pressure on a human to be able to actually focus, to be actually be able to do deep thinking and then to be able to remember everything they did, they did, it’s only getting, you know, m- more and more challenging is what we realized.

And so, for us, we focused on the observability layer, and then from there, we spent years figuring out how do you piece that data together to actually make sense of it.

Bim Dave: Hmm. It, it’s interesting. I, I wonder, like, how much a firm actually genuinely understands about the, the kind of percentage of what, what lawyers do on a day-to-day basis, right? But it sounds like this is a, a great way of being able to understand a little bit more about working patterns and using that data to be able to actually improve, improve their working experience, right?

Kourosh Zamani: Yeah, I, I would say so. And, you know, in the phase one of the business, Bim, it was all about, like, automating time or time sheets, uh, as a value driver. It’s like solve a real problem, drive economic value to the firm, um, and that earns you the right to then go to the next phase of the business, uh, uh, for us, which was then building a time intelligence platform.

And it’s exactly what you said. Um, what ultimately gets on an invoice or a time sheet, like, it’s a fraction of the, the valuable insights that you can have if you can actually see the way that people are working, what tools they’re using, what their workflows are, where the connective tissue is among different people within a firm.

Uh, and so for us, it was like solve the biggest pain point, which at the time is time sheets, impact the revenue driver, but that, that earns you the trust and the right to then go solve these other problems, which is what you mentioned, which is understanding how work gets done.

Bim Dave: Yep. And d- would you see– would you say that this is like, um, just a visibility issue, or is it actually that firms are losing revenue as a result of not having the right tools in place?

Kourosh Zamani: Oh, one, 100… Well, both, but100% losing, losing revenue. We’ve done now hundreds of studies and, uh, we actually have an entire data science team that’s focused on this. We, we run what we call an ROI analysis. What we do is we can compare users pre- and post-adoption of Laurel, um, into their workflow. And what we have found are, like, three pretty mind-boggling statistics.

Uh, the first is that our average user, now, you know, this is across 50,000-plus users at this point, they end up accounting for an extra 28 minutes a day of billable time that would otherwise be forgotten. Um, that can derive from anywhere from, like I mentioned, waking up in the morning and doing work that you forgot, or your, your kid’s soccer game and you’re taking a phone call, or you have a 30-minute meeting scheduled on your calendar, it runs for 45 minutes.

Uh, 28 minutes more billable time is getting picked up. That has economic value. The other statistic is that we’re saving people anywhere from one to two hours of admin work, and that time can get literally, like, repurposed into anything. Some people decide to m- work more, some people decide to go to the gym, some spend that time with their family.

Uh, that ties back to our mission as a company, which is to return time to the world. So we’re really proud of the fact that we can take one to two hours of time that is literally not creating any value in the world. It’s administrative work to get credit for the actual work that you’ve done, and we just make that, we make that go away.

Uh, the third piece that we’ve seen is Clients are getting very sophisticated, not our clients, but the customers of the, let’s say, the legal industry, uh, in, in the way that they review bills and the way that they vet and screen bills. Uh, and they have quite advanced AI technologies to do this now. And so for us, what’s really important is, uh, the business isn’t just about finding more billable time.

It’s about constructing the output of that into a way that is what the, what the end client wants to see. Are the bills submitted in a timely manner? Are they written in a way that they want at the right fidelity level? And so a lot of our effort’s gone into understanding what are called outside counsel guidelines.

Think of these as, like, rule sets of how customers want to get billed for and what they’re willing to pay for and what they’re not, and being able to extract the most relevant parts of those rules, bring them to the forefront of the way that timesheets get created. And that’s been a really important filter for us.

And by doing that, by bringing that to the point of inception, we’re actually seeing realization rates go up about four to five percent for our customers, which alone has a huge impact, uh, on the firm’s revenue.

Bim Dave:Yeah, that’s massive. That’s massive impact, right? Um, I think this is one of the common things that I hear when I’m talking to the law firms in our portfolio of customers, is that compliant time is probably like one of the, the major headaches that they’re having to deal with. Um, and when you, when you think about all of the, the various terms and conditions that are, that are, signed up to, right, with their clients and how you kind of track and manage those at the point of time capture, and particularly when you then add on the co- the, the kind of, um, the distribution of the bills, right?

When it becomes the l- goes to an e-billing solution or whatever, um, the, the kind of cost of that getting rejected is, is high, right?

Kourosh Zamani: Uh, you wouldn’t believe it. I, I literally– I mean, when we started the business, oftentimes the people that reached out to us were more like the CI-CIO, CTO innovation profile. Um, today I’d say more and more CFOs are reaching out to us because they’re the ones that are getting stuck having to sit between the firm and the customer, uh, and dealing with billing disputes over literally the use of a word or the placement of a semicolon.

And it is, it is not where anybody should be spending their time. And so they’re reaching out and saying like, “How do we clean this up? How do we get rid of this, this, this pain point that really shouldn’t even exist for both sides?”

Bim Dave: Mm. Yeah, no, ab-absolutely. So, so Kourosh, I want– I’d love to just kind of, um, pivot a little bit to kind of discuss the topic of now, which is AI and the future of knowledge work. And I’m interested to get your take on how AI changes the relationship between effort for a lawyer and value.

Kourosh Zamani: Yeah. I mean, it’s completely shifting the way that work is being done. Um, structurally, it’s forcing the industry to change, right? Because Time had been a proxy for value, and that’s no longer the case. I think time will always be relevant, but not for the purpose of, uh, building a timesheet. It’s for the purpose of understanding, like, how effort was exerted.

Um, it’ll always be an input into cost, which is going to be important to understand margin. So for sure, it’ll always have a very important place, but it goes more from, like, this unit of value around time itself and more of becoming indispensable as a data source. Uh, especially if you can tie the time to observability into how work is actually getting produced.

Why that’s important is, like, more and more we’re getting asked, “Well, what work was AI-generated versus human-generated? What work should shift from humans to agents? Uh, what work should be outsourced? What should move upstream or downstream?” You can’t improve what you can’t measure. And so for us to have the agent that we’ve developed that provides observability into all of that, what we do is we create a data set that can then be used to inform the structural changes that need to be made in the industry.

Where that’s going, what exactly that will look like, it’s changing very quickly. But what everybody knows is you can’t operate blindly in that new environment. And so the firms that we see are being proactive are the ones that are acknowledging that the change is happening and realizing they don’t wanna enter that new space blind.

They want the observability that’s machine-generated, not human-reported, uh, and in real time. And so that’s where we can help on that front.

Bim Dave: Mm. Indeed. So what, what, what do you think happens to the billable hour when, when we see AI kind of compressing some of the work dramatically? What, what’s, what’s your view on that?

Kourosh Zamani: Yeah. I think time’s always been recorded, but it’s never actually been understood. And so, I– for us, like, if there’s one thing I can do in the world, I hope that Laurel will accelerate, uh, the billable hour going away when, when the time comes for that to happen. Uh, in the meantime, we, we have to track time as a unit of measurement.

We’ll continue to do that. Uh, but I want timekeeping to become, or let’s say the billable hour to become irrelevant as a task, but indispensable as a data set. I think that’s the, that’s the key piece that we want everyone to realize.

Bim Dave: Mm. Do, do you think that firms are prepared for a world where, like, outcomes matter more than the hours that are recorded?

Kourosh Zamani: Are they prepared? They’re trying to get there, but it w- it ironically, it will take time, um, is what I would say. Uh, I saw more companies, more law firms were created through Y Combinator in the last year than, you know, something like 10 new companies blossomed just out of Y Combinator. I mean, that, that is a crazy statistic.

When, when we applied to YC sometime in 27 twen- 2017, 2018, there were very few legal tech companies at all, um, that were trying to build, and now you have law firms emerging out of Y Combinator. So what that tells you is, uh, the change is happening both bottoms up at that level and top down, and we’re seeing it, uh, among the AM Law as well.

The challenge is like there are a lot of structural changes that need to happen for this to work, and it’s, it’s not just about time sheets and the billable hour. It, it’s everything from how people get compensated to how they get evaluated, uh, to how work is distributed. You know, if you’re still measuring a- an associate based on how much time they bill, are they really incentivized to build efficiency?

It’s like the exact opposite of the mindset that we have here internally, where we, we, uh, we give everybody as much AI tooling as they want, and we literally encourage them to automate their job. And if they can, they can literally get rid of their job. There’s a huge reward on the back end of that. And so incentives drives behavior, right?

Or drives behavior and drives change. And so I think there’s a lot of challenge around this still in professional, professional services.

Bim Dave: Yeah. No, 100%. I, I love– And I love that approach. Like, that’s the right incentive structure, right? Um, so, so what, what about– what impact does this have on, on profitability for firms? Like, how should firms be thinking or rethinking about profitability in, like, this AI-driven world?

Kourosh Zamani: Yeah. It, it’s all about you still need to understand your human cost and not at a… Like of course, you have salary data, you have, you know, the cost of benefits and office space, and you could build high-level models around that. Um, but you actually have to understand the way that the work is happening with, with precision.

And I think that’s really important. And you need to eventually, like have that just happen in an invisible state continuously. Uh, it’s a big reason, uh, when we started the business, we targeted time and materials-based industries, right? But why I’m confident that the legal industry will evolve is because we’re getting approached by many other verticals now, where they have never billed for a time sheet.

Um, I’ll give you an example. It might be an investment management company that’s trying to understand the way that their top performers allocate and spend their time, uh, versus their bottom performers. Um, and then in terms of like the success that they’re seeing from an investment standpoint or company selection standpoint.

Well, it is a very interesting problem. They’re analyzing work data to try to understand the differentiators there, right? Um, but they’ve never been interested in understanding time for the sake of billing it. They’re trying to understand time for the sake of understanding how to run their business differently and understand where their bottlenecks are And which workflows should go away versus ones they should double down on.

To me, that’s really interesting, and it’s a signal in terms of where TNM-based industries are gonna go. They’ll be forced to eventually think in the same way. And for us, we’re trying to take things we’re learning from other industries and then bring that back into spaces such as legal or accounting, where time is still a proxy of value.

Um, but I don’t think we’re gonna learn how to solve that within that space. We have to go learn from the outside and then bring those best practices in. And I think that’s been a really fun challenge for us.

Bim Dave: Hmm. So do you think that the firms that actually survive will be the ones that really have that deep understanding of their data the best?

Kourosh Zamani: Yeah. I think time will not be something that you enter. It’s something that you’re going to query. Uh, and that is– that will be the end state of where we, where, where we end up. There, you know, the, the interface doesn’t matter, but the dataset very much does.

Bim Dave: Yep. Indeed, indeed. Um, I’m– So I’m interested to get your take on, on, um, the kind of automatic aspect of, of time tracking and work tracking, um, generally. Um, how, how do firms balance like the– There’s, there’s the kind of the benefits of the operational intelligence that you get, right? Which I completely understand and makes sense. How do they balance that with then the employee trust that they’re not being seen as kind of being, um, almost like surveillance, right, of, of, of the workday? How is that typically addressed?

Kourosh Zamani: Yeah. B-I mean, Big Brother, like even from day one, it was a concern that comes up. It’s, um… I, I think the world has changed such that it’s like, it’s less of a concern generally because people are so excited about the tooling that’s becoming available. But to, to answer your question directly, um, we– One, like we drafted an ethical stance as a company very early on, and we still stand behind it.

Like, we have no interest in being a surveillance company. Uh, and so how have we, how have we addressed this tactically? Um, one is when we do implementations, we very clearly outline and we give visibility to the user of like what tools and applications are going to be picked up and which aren’t. Um, and this is even down to a URL level, for example.

Uh, your Amazon shopping list during the holidays h-has no, no purpose ever being on your timesheet, and so it will never even be picked up. Um, and so one, we lead with transparency. I think that’s really important. On the timesheet side, uh, I think a lot of, like most people don’t know this initially, but everything that we, um, predict, we present back to the human.

And so they’re one hundred percent in the loop, and they’re the only one that sees that draft form. So they have a chance to edit it, revise it before it gets submitted into a system of record. That’s a, a really important distinction. And, uh, it’s not that AI is just 100% accurate in creating your timesheet and submitting it for you, but it’s gonna get you 80, 90% of the way there.

And so that as a human, you just go in and you can do a very efficient review and submission. The last piece is on sort of going into the observability layer that we’ve been talking about. Um, for us, anytime we’re doing that, the data is being, uh, anonymized and aggregated at a per firm level and, and not commingled in any way.

The reason that’s important is you can still glean insights without actually having to look at an individual user. You can look at practice groups, geographies, seniority levels. Um, all of that is still incredibly useful, but you don’t need to have any type of like big brother surveillance at an individual level.

And so that’s where we’ve put our focus in terms of, um, the dashboarding that we’ve been developing.

Bim Dave: Yeah, makes total sense. Um, so you, you mentioned earlier that, that you guys see yourself as a data company. What, what are you seeing, like some of, some of the most sophisticated firms, um, that you’re working with, what are they doing differently with this work data that kind of sets them apart?

Kourosh Zamani: Yeah. The– there are a couple things that we’re seeing. One is, uh, I think there’s like this reckoning that is happening where a lot of AI pilots have been launched. There was a lot of excitement around it and like we’re approaching renewal season, I mean, in a, in a very massive way more broadly. And so I think the firms that we see that are ahead of the curve, um, are asking themselves like honest and very hard questions around what, what is the ROI of the AI that we’ve invested heavily in?

Um, and this isn’t about like, “Hey, let’s do a survey and ask people if they feel good when they use AI.” That’s not, that’s not the kind of like, uh, interrogation that we’re looking for of the data. The ones that are a step ahead, they’re going, they’re going way deeper than that, and I’ll give you an example that’s interesting.

Um, so we had a customer. They’re, um, one of the big four, and they invested heavily in tooling, um, one of which was Microsoft Copilot. And so it– they had a massive deployment and obviously were facing like a renewal decision around this. Um, and interestingly, like they knew what licenses they’d purchased, right?

They had visibility into that. The provider of their AI tooling could tell them maybe what got activated in terms of whoever logged in or how many times a day were their interactions. But they had no way of knowing how is the tooling actually being used, and then what work product is being generated as a result of the tooling.

And for us, we re- we sit at a very interesting place where we sit between… We, we will be systems of action, but we sit between systems of action and systems of record, which is ultimately, uh, the ERP where this work gets built. We have observability end to end across that And so not only can we tell you who used which tooling, how much did they use it, but we can actually see how did they use it and how did that shift workflows?

Did it reduce administrative work? Did it increase billable work? And ultimately, to your question around pricing, like what got paid for this work on the back end? That’s very interesting to see. And so we’ve built, uh, the capabilities to be able to do ROI, uh, AI ROI analyses for customers around tooling sets.

Uh, and eventually we’ll get this to a very automated state where it just lives in a product and CIO, CFO will have access to it. Tonight, we’ve done a lot of design partnerships around this, and we’ve been developing tooling, uh, in tandem with, with them. Uh, in particular, we found, like this is just one use case, uh, that the firm that was using, uh, Copilot, the cohort that was using it regularly ended up actually billing two point three hours more per week per user than their peers who were not, which was quite astounding.

Uh, and a large portion of that we saw was a shift from doing administrative work, which was non-billable, into billable work. And so it was a good use case of not only if you reduce admin work, uh, is that just a general net positive, but does it move into being billable? And we were able to show that it directly was.

And so, for them, we quantified the value of the AI tool that we bought– that they’d bought, which allowed them to make a very quantitative decision around a renewal. That was a very massive renewal decision. And so I think you’re going to see more and more of that happening, where leadership interrogates the data around their usage, and they really press hard on what is the value that they’re seeing.

Uh, and they’re going to look for tools such as Laurel that can provide that observability.

Kourosh Zamani: That’s the goal. So right now, where we’re at is as we go into, uh, legal and/or new verticals, we’re deploying teams that can go and understand what we call the ontology of work. And so it’s taking specific workflows and then being able to segment that into specific steps. Generally, um, you’ll find some overlap between industries, but many times this will be, uh, vertical specific or firm specific, depending on the workflow.

And by observing the way that work’s done, you could start to find the trend lines to build this ontology. What’s great is you can then look at the specific workflows, and you can make predictions on if there’s value to automate it versus not, and the difficulty of being able to automate the task. You could associate a quantitative ROI or dollar value to that.

And that way, as a CIO or CTO sitting there deciding, okay, what is our technology roadmap? Uh- It’s not guesswork anymore. They can literally pinpoint a step in a workflow, see how many people across the organization, uh, interact with that step, and what would the value be if they were able to remove that step completely or automate it using technology.

That then informs their buying decision. And so for us, this is act two of our business, which is act one, as I mentioned, was time automation. Uh, act two is time intelligence, and that’s like a very specific use case, what I just outlined of, uh, where you’re gonna see Laurel going.

Bim Dave: Very good. W- uh, so when we think about, um, some of the intelligence that we get from, from the data that we’re, we’re kind of, uh, leveraging, how important is data quality before some of that AI becomes useful for a firm?

Kourosh Zamani: Uh, the quality is quite important. It is, um… For us, when we, when we were doing the focus on the timesheets, the reason that we had a distinct advantage is, uh, it’s one thing to have just activity data, right? But to have it actually grouped and matched to a project or a matter, that human-in-the-loop was very important tagging for us.

It helped build ontology in a sense, like that was ontology for legal, being able to do that. Uh, and so that categorization, I think, is a really key point. Um, and it’s where we’ve been investing heavily in terms of some of the models that we’re building is, how do you take raw activity data and then meaningfully tag it in a way that is useful, uh, based on a project or a step in a workflow.

And once you have that, it then allows you to do more meaningful things with the data versus just seeing, uh, touch points of activity in isolation. I think that was a limitation historically that, uh, sort of prevented us from getting to the next phase, uh, with other capture products.

Bim Dave: Mm. Yeah, I, I read a lot of stats about kind of failed AI implementations, and I, I wonder whether firms actually have like a problem with the AI solution or versus like a data maturity problem, right?

Kourosh Zamani: Yeah. Yeah, and for us, you know, I’m just gonna kind of like drill into what you just said.

Bim Dave: Hmm.

Kourosh Zamani: that you’re realizing or everyone’s realizing is how important context is for any usage of AI within an organization. Um, if you deploy a tool that doesn’t have context, it outputs results that are irrelevant or not, you know, really not useful to the, the practice of the professional that’s using that tool.

So, what you’re seeing is a lot of the front team, frontier models deploying tooling that can then embed into the ecosystem of a firm to gather context, right? They’ll pull in emails. They’ll pull in calendar data. Um, you’ll give it access to certain folders on your drive where that you can upload information for context.

I think what makes Me really excited about what we’re doing is we have what we call last mile work data context. So it’s not only like the broad purview of the data lake in terms of what these frontier models want to access, but you’re actually seeing like specifically today, yesterday, over the last week, what were people doing?

What, what were they, what were they touching? Uh, what work product did they produce? And that recency context is a really important layer to then have good outputs on any AI tooling that the firm’s adopting. And so as I think about the next phase for us, I’m really excited to see how do we take that last mile work data and make it indispensable for our customers so that they get value out of the other AI tooling that they’re investing in.

We want to make their experience better with those other tools by giving them access to really clean, structured data that was previously unattainable. And so it’s, it’s exactly what you said, Ben.

Bim Dave: Yeah, no, that’s, that’s great. Um, and, and very– I think that, that opens things up, right? In terms of what capability you give a law firm to be able to understand their data, um, in, in– and, and help to, to kind of clean that data. You end up in a very different world. So that’s, that’s really good to hear. Um, let, let’s switch gears and talk a little bit about the future.

So, from your perspective, um, what will a professional services firm look like in the next five to 10 years?

Kourosh Zamani: Yeah. We’re– I mean, we’re seeing it today. It’ll be, uh, tighter, highly capable teams that leverage AI meaningfully to do any work where systems such as Laurel have identified are repeatable and automatable. And then you’ll still have domain experts to handle the things that machines will never be able to do.

Uh, managing some of the complexity, dealing with people, influencing emotions. Like these are the things that will never go away, and they– the largest, hairiest, most challenging problems will always be human-driven. But all of that work will be supported by, uh, tooling that allows them not to spend their time doing things that we weren’t put on this earth to do.

Uh, we could automate that away and then focus on the things that are distinctly unique to each of us and why– what our purpose is here.

Bim Dave: Mm. Indeed. Do, do you see, um, like any impact to certain operational functions that you think will just disappear entirely as a result of AI?

Kourosh Zamani: I mean, certainly, certainly. I think, uh… Look, you’re already seeing it happen across the technology industry, right? So when we look at Meta, uh, Oracle, SAP, Workday, the, the volume… Even Amazon. Like the volume of, um, the layoffs that have been happening, it, it sort of signals to you like the reality of the world, right?

These companies are being forced to reimagine the way that they operate Uh, and they’re being forced to become leaner and more efficient. There’s no question that that will eventually flow into professional services if it, if it hasn’t already. It’s more of a, a maybe a lagging. Like, if those companies are your leading indicator of what’s to come, uh, there’s no question that it’ll, it’ll flow.

But, um, you know, for years you’ve seen even consulting industries under pressure, right? And so, like, the compression will happen. Um, I’m not saying that the net sum of value creation will change. It’s just that the economics and the way that the work is produced and the way that it’s valued, that has to evolve.

So, it’s not like these industries will shrink from a, a, like a GDP net value creation standpoint. It’s just the way that they operate and the mechanisms by which that value is created. It, it h- it has to change, and I think you’ll see an acceleration of that is, is what I would predict.

Bim Dave: Mm-hmm. Yeah, no, I, I agree. so so for the, for the managing partners that are listening to us today, um, what advice would you give them to kind of prepare their firms for a success, you know, to be in a successful state in the next 10 years?

Kourosh Zamani: Yeah. It… The advice is that you can’t improve what you can’t measure, and that you should put– you should have the tooling in place to have very strong observability into the way that your firm’s generating profitability and revenue, uh, and into the way that work is being produced. And that second layer is very important because when you can see the way that work is actually being produced, you can decide, uh, what portions of it you should hold onto, what portions you shouldn’t hold onto, uh, and you can understand profitability at a, at a, uh, granularity that was previously unattainable.

And I think that precision is going to be important. It’s precision and understanding work data, um, that is something that you– we can’t do without anymore. You can’t run blind.

Bim Dave: Yeah. Indeed, indeed. So tell us what’s, what’s next for LORAL? What’s the, what’s the exciting thing that you’re working on next as a, as a company?

Kourosh Zamani: We will always continue to enhance the core timekeeping experience that we have. It’s so important. We’ll continue to evolve the way that we do our observability through the agent. Um, one of the biggest differentiators for us has been multimodal collection. So think of that as, um, depending on how it’s configured, AI that can see, listen, and read.

That’s really important if you’re trying to grab the entire digital footprint of professional work. Um, and that was always for us phase one, and now we’re in phase two, which is building this category around time intelligence. And what I would love to see is the consulting firms in the world building entire practices around the insights that we’re driving so that they can advise their customers on how to better operate their businesses, how to deploy an AI roadmap, and how to reshape the way that work is done.

And so, if we’re truly a category-defining company, the data that we’re providing and bringing forward will be so rich and so helpful that entire consulting practices will be built around this to help the world accelerate their adoption of AI. And really, what’s ahead, you ask what’s on, on the horizon?

Uh, it’s continuing to drive value from our customers from the data exhaust that we have. And so how can we be the very best context layer for them so that when they’re investing in other tooling, we get the most– they squeeze the most value out of that tooling, uh, as, as possible. And we would love to help with that, and that’s what’s next for us on phase three of the business.

Bim Dave: I love it. Exciting times ahead. So we look forward to seeing how, how you guys evolve.

So, uh, just a- so just couple of, um, wrap up questions, um, before I let you go. Um, first of which being, if you could go back to yourself at age 18, what advice would you give yourself?

Kourosh Zamani: The advice that I would give to myself is one that I actually ended up doing a little bit later in life, but it, it is, uh, it is to just not be afraid to make mistakes and to embrace your naivety, ’cause that is very important. Uh, the older you get, it seems like you have to know more of what you’re doing and where you’re going, and it just makes it a lot harder perceivably to make… you know, presumably to make mistakes. And when you’re young, I think you should embrace the fact that you have, uh, the benefit of the doubt to make mistakes, and so you should capitalize on that ’cause it’s a really– it’s a very precious time to be able to do that.

Bim Dave: love it. It’s good, good advice. Um, and your favorite travel destination?

Kourosh Zamani: Oh, lovely. Uh, it is currently Italy, and that’s because my co-founder got married there last summer, and our CEO, my other co-founder, is getting married there next month. And so we get to go there twice in about a year, uh, to celebrate a special moment for two of the most important people in my life, and that, that means a lot to me.

Bim Dave: Amazing. Yeah, brilliant. Yeah, good, uh, and a good choice. A good choice. Um, and finally, what’s keeping you inspired right now outside of the legal world?

Kourosh Zamani: My team. Um, we do regular demo days every other week where we showcase, uh, literally, like, what, what we’re building and what we’ve built. And a year ago, if you looked at it, it was the product, uh, design and engineering team that was doing all of the demos. And now we’ve reached a point where when we do these demo days, I’m seeing more and more go-to-market demos where we’ll have AEs, BDRs, uh, CS folks joining the team, and within weeks with the tooling that we’re giving them, they are building Uh, automation tools that are scalable and deployable, and they’re sharing it with their colleagues to save hours of time.

And it’s, it’s quite jaw-dropping, uh, to see what’s being produced internally. And we still have work to do to get better, but I’m very proud of the company, uh, and how we’re pushing the limits on what it means to be, uh, an AI native startup, uh, at this point. And I’m, I’m very proud of that. And so my team’s inspiring me every single day.

Bim Dave: Amazing. That’s really good to hear and, uh, a lovely thing to say about your team. I’m sure they’ll be, uh, very happy to hear that. Kourosh, it’s been amazing to have you on the show. Thank you for spending time with me today. It’s been great to learn more about Laurel and your platform, and I’m really excited to see how you guys grow in the future.

Kourosh Zamani: Thank you, Bam. I, I really appreciate that you do this podcast. You obviously have a wealth of experience in this space, uh, and it’s great that you continue to push the envelope and bring in people to exchange ideas with. So thanks for hosting me.

Bim Dave: You’re very welcome. We’ll speak soon.