AI Agents Are Replacing Finance Workflows While Revenue Gets More Complex with Ali Hussain
In this episode of Future Finance, Paul Barnhurst and Glenn Hopper speak with Ali Hussain, CEO and co-founder of Tabs, an AI-powered platform revolutionizing the revenue process for finance teams. Ali discusses how the traditional ERP systems have left revenue management behind, and how Tabs is changing the game by automating the full contract-to-cash cycle. He shares his insights on the complexity of revenue management, AI’s role in financial systems, and why understanding the data context is key to success in AI-powered finance tools.
Ali Hussain is the CEO and co-founder of Tabs, where he leads the development of AI-native solutions designed to automate the billing, collections, and revenue recognition process. With a background spanning product leadership at Google, strategy consulting at BCG, and public policy, Ali is at the forefront of the next wave of finance technology.
In this episode, you will discover:
How AI is transforming revenue management in finance
The importance of data context in AI-powered tools
Why traditional finance systems fail to address modern revenue models
How Tabs automates billing, collections, and revenue recognition
Ali Hussain shared invaluable insights into the future of finance, highlighting how AI is revolutionizing revenue management and financial operations. While AI brings immense potential for automation, human judgment remains crucial in making strategic decisions.
Follow Ali:
Website: https://www.tabs.com
Linkedin: https://www.linkedin.com/in/ali-hussain786/
YouTube: https://www.youtube.com/@tabsplatform
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn - https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://bit.ly/4i1Ekjg
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[00:00] – Trailer
[03:00] – Legacy Finance Systems
[06:15] – Revenue Management Gaps
[09:00] – How Tabs Transforms Finance
[12:00] – AI in Billing & Rev Rec
[15:30] – Clean Data Importance
[19:00] – AI vs Human Judgment
[22:30] – The Future of AI
[25:00] – Complex Revenue Models
[29:00] – AI Challenges in Finance
[34:00] – Closing Thoughts on AI
Full Show Transcript:
Host: Paul Barnhurst (00:00):
Welcome to the Future Finance Show where we talk about treasury management. Future Finance is brought to you by Q flow.ai, the strategic finance platform, solving the toughest part of planning and analysis, B2B revenue, align sales, marketing and finance seamlessly speed up decision-making and lock in accountability with Q flow.ai.
Co-Host: Glenn Hopper (00:41):
Welcome to another episode of Future Finance. Paul, I've been back to back meetings all day. I don't even know where I am. I'm going to be leaning heavily on you in this episode to really keep us on the rails and welcome our next guest and let me know if I just fade off and start rambling. Well wait, that's what I do on every episode
Host: Paul Barnhurst (01:01):
I'm going to say, so what's changed Glenn? Just kidding. No, great to see you, Glenn. Excited to be here with you and this week we have Ali Hussain with us. Ali, welcome to the show. Thanks
Guest: Ali Hussain (01:12):
Glenn. Hey Paul, both of you. Thanks for having me. I'm very excited to be here. I'm glad I got both versions of you today.
Host: Paul Barnhurst (01:17):
Well, thank you. We're glad you're joining us. I know we had hoped to record this a little earlier and had to delay it, so I'm glad we could get our calendars back together and make this work. So this will be a lot of fun.
Guest: Ali Hussain (01:29):
A hundred percent post-tax season. Always a better time to roll through. We'll have more listeners as well.
Host: Paul Barnhurst (01:33):
All right, so a little background about Ali and then we'll jump into things. So Ali is the CEO and co-founder of Tabs, the first AI native platform automating the full contract to cash cycle for modern finance teams. Before founding cabs, he served as COO at Latch where he scaled the company from seed to IPO and experienced the inefficiencies of legacy accounts receivable systems firsthand. His background spans product leadership at Google Strategy Consulting at BCG and public policy at tabs AI is modernising the 125 trillion. How is that all global B2B revenue market? By replacing fragmented finance tools with intelligent AI agents that handle billing, collections and revenue recognition under leadership tabs are scaled rapidly, crossing 1 billion in annualised billing volume and serving over 300 customers. ALI is emerging as a leading voice in vertical AI and the future of finance and infrastructure. So again, welcome, love the background, love what you're doing at cabs and really excited to get some of your thoughts today.
Guest: Ali Hussain (02:52):
I appreciate it. No, I appreciate the bio and thanks for introducing us. And I think the cool tidbit is even when we wrote that it was close to a billion I think, and now we're close to 5 billion. So when you just think about the velocity of how much money is moving in systems that are now leading on ai, it's been amazing to see the scale in the market.
Host: Paul Barnhurst (03:09):
Well, it's exciting. Congratulations on closing in or getting that 5 billion here's to 50, a hundred, 200 wherever it ends. Right?
Guest: Ali Hussain (03:17):
You said that multi-trillion number, I'm not even scratching the surface yet.
Host: Paul Barnhurst (03:21):
So talk a little bit about why you started tabs. I mean your background's consulting and operations, not finance. So why a finance company?
Guest: Ali Hussain (03:29):
It comes out of being a humble observer of just different systems and one of the things, and you mentioned earlier I'd been a COO before of a B2B business in which one of the things the COO gets to do is go wide versus just deep. And one of the observations when I was helping oversee enterprise applications is I got to see what the CFO was buying in ERP. The CRO was buying in sales tooling and the CTO was buying in product and engineering tooling. And for me the fascination always was how much I saw a lot of the ERP and kind of the way you buy and service those products kind of lag. And this is well before ai we're talking a decade ago and people were buying, NetSuite and then on the other hand you have folks buying HubSpot and Salesforce and we started to see things like AWS and different cloud products come out.
(04:19):
And so part of my fixation was seeing a lot of the breadth in different parts of the stack and always feeling like finance was being changed in terms of innovation and product support that was coming into the market. And so that was a combination of seeing where I felt that. And the thing on top of it, and what's fascinating, Paul's office of C-F-O-E-R-P spend is maybe the highest. So it's not like finance is underpaying for these systems. If you look at overall finance and ERP spend, it is more than the market caps of Salesforce and HubSpot combined annually. And so that reconciliation of seeing other areas of enterprise apps advance and seeing the ERP lag was an area why I was hyper fixated on trying to build a company here. Specifically revenue then came to being the partner to the CFO and seeing how they were going about their ERP stack.
(05:13):
I noticed a very interesting trend in the business I was at, which is a lot of the use of a single ERP was unbundling and I saw that happen with AP and spend as we left NetSuite's procurement module and AP module and went to products like Coupa and ramp and obviously pure loan benefits has always been in that bucket, but we went from a world where you could not move unless you went to Workday or a DP with products like rippling and Gusto and a handful of modern stack. And so revenue just when I decided I wanted to go build a company bigger, better, faster finance systems is the largest bucket of spend in the world. And then on top of that, I had this fixation with the revenue side being way behind the vendor management and employee management side of the ERP and that was kind of the precipice for going and wanting to start taps.
Host: Paul Barnhurst (06:03):
So it was really, you were looking at a problem within that ERP within the whole finance system and revenue was the area that kind of fascinated you in the sense of where you felt that's the area to focus.
Guest: Ali Hussain (06:15):
Exactly. I had full fixation seeing that. I saw what happened with the unbundling of my ERP and I saw modern platforms like Rippling Ramp and revenue was like it just doesn't make sense. It's not like revenue is a second class workflow, it's the lifeblood of the business, but there's no equivalent product tooling to what I saw in AP spend and payroll on the revenue side.
Host: Paul Barnhurst (06:39):
Got it. Makes a lot of sense.
Co-Host: Glenn Hopper (06:40):
So Ali, we were talking before the show and it's completely unrelated to this podcast, but I feel like Tabs is everywhere right now. And this is just anecdotal, but I just had a tabs demo with a client and we're looking at a tabs partnership through my firm. Again, not a sponsor, this was just happening through the natural ecosystem of business. So I just need to bring that up. No news conflict of interest here or whatever.
Host: Paul Barnhurst (07:06):
No money has exchanged hand,
Co-Host: Glenn Hopper (07:08):
But because of that before we talked, I've got a pretty good idea of what Tabs does, but I realised we gave it the once over in the intro. But if you could for our listeners, maybe walk us through what problem you're solving for businesses and what Tabs does at maybe a little bit deeper level than what we talked about in the intro.
Guest: Ali Hussain (07:28):
So happy to do that. So ultimately the problem we solve is we believe that at the end of the day, if you're running finance and accounting, there's three kinds of core operational layers. As I mentioned earlier, there's how you manage your spend, how do you manage your employees? And really it's how do you manage your customers from a finance standpoint. Historically, most companies manage their customer relationships in CRM, whether that be HubSpot or Monday or Salesforce. The problem with those tools is they're really good at lead to close, meaning how do I drive a lead? How do I demo them and drive them to a closed one deal? But they're not built for finance and accounting teams, which are really the performers around the obligation after an account is closed, one meaning you've taken the customer, they've actually decided to move forward and sign up for your product.
(08:17):
And so what tabs does is it effectively automates everything after the day of closed one. And what that means is we use an AI layer, which is our commercial graph to read and structure all of your order forms, your contracts, whatever way you do business with your customer and that's the key wedge of our product. So before ai, the reason people often say is why was revenue so left behind when bill.com came out and RAM came out and Brex came out or zip came out and the reality is revenue or that customer relationship, Glenn, as I'm sure you've seen on these demos, is a highly complex commercial relationship and unlike just scanning a receipt or understanding payroll, this is very complex and intimate. And so AI starting in 2023 started to give us the technology to read and understand that. And so historically you would have a finance team who would've to go read contracts and type in the billing terms or the products or you would pay a third party service provider to go in and duct tape into your Salesforce order and contract objects and try to pull that data that a sales rep typed in and it was never accurate and always missing things.
(09:25):
And so our whole magic is we go in and we read these documents using AI and from there we structure and organise them within tabs. The primary use case for many folks who use us is to then run all billing. So that goes and creates invoices. If there's a usage component, we can compute the invoice. We handle all of the communication which accountings offered referred to as Dunning all the way to payment and cash apps. And so we do that on top of that. What makes us a revenue platform versus a billing platform is because we have all of that intimate kind of contracts, amendments, renewals, we can then understand all of the performance of those products from a 6 0 6 standpoint because we're not just capturing billing terms, we can understand contract terms, we can understand different types of performance obligations, amendments, and then we can run your deferred revenue.
(10:18):
And because we have that deep data within the system, we also know: is it a flat fee product, is it a usage product, a tier product, a milestone-based product? We can appropriately allocate that revenue and do much more comprehensive complex rev rec than most systems. And then obviously all that data lives in the system. So we can do a lot of non-GAAP reporting, your cash forecasting, which is often the most missed metric. People often ask, what's your favourite part of the product? It's actually cash forecasting because we have all this amazing data, we can give you a very clear view of how much you'll collect each month on a 13 week basis. And so those are the different parts of revenue that we help companies just scale with a lot leaner teams and avoid doing a lot of manual work.
Co-Host: Glenn Hopper (11:06):
I'm going to reference something we said off air again because we were talking for a couple of minutes before the show, but we were, Paul was mentioning what being an AI company right now and the LLMs frontier models are getting so much better that there's this, well, why couldn't I just vibe code that myself? And I'm thinking about exactly what you're talking about. This wasn't an AI job, this was just some basic fractional finance work. I was doing the company and we were going through and trying to use an LLM to find some contract, several hundred contracts. Some had one specific line for cancellation and terminate anyway, it was when you're trying to chunk and vectorize and go through all the data, it was very hard to find that specific use case that we were looking for. And it was interesting. We thought it would be something that an LLM would have a really easy time doing, but just the way that the chunk, and it was some were pdf, some were like the image kind of anyway, it was a total nightmare of a situation and for you guys to solve that at scale, I always have a hard time not getting into making the sausage here.
(12:08):
And obviously a lot of that's proprietary too, but this isn't something somebody's going to just go vibe code. And
Guest: Ali Hussain (12:14):
I think it goes with my philosophy, right? I think LMS are amazing tools particularly for reporting once the data is cleansed and structured and understood from multisystem, multi contract, et cetera downstream. When I think about finance use cases, I was at a talk recently with the CFO of philanthropic Krishna. They're using LMS for amazing reporting, but they're still using traditional systems to run core accounting workflows. And so back to your point, contracts taking them over and putting them into operational parts of finance are very hard to do with just yourself out of the box with lms. And the reason is by nature LMS are non-deterministic, but there things like invoices and rev rec are highly deterministic and I think there's a reason why a lot of them have been very effective in legal and areas of just understanding a simple clause and trying to get an alternative to that clause and those type of workflows.
(13:18):
But something to get to a level of taking pricing out of a contract and producing an invoice. The problem with a public LM is you can do it three times and you'll get three different answers and that's very difficult. It's still cool and you get some insight very quickly. And so a lot of where we think the best finance systems are going to be highly defensible in the world of public LMS is really tightly built context graphs which use lms, but they also use other types of ai. So when you look at our commercial graph, there's a lot of last mile machine learning that is required to get to consistency so that if you load those 200 contracts every time an invoice comes out or every time we build your deferred revenue from a gap standpoint, it is the same over and over again in line with the memory and the direction you've given on how to infer your contracts.
(14:13):
And so we think that billing is a really bad use case of out of the box lms, but once we have all of your data, right, we're happy to give it to you and the way you want to manipulate that to do reporting, it's an amazing use case. And so that's one of the ways many of our customers have been able to live the best of both worlds where they rely on tabs to get the contacts, read contracts, build invoices, do all their rev rec, but then we can give them their data to do a lot of their FP and a lot of their reporting in ways where they can pull data out of tabs and Bill and gusto in a way that otherwise would've required a lot of tooling and a lot of human labour and excel.
Host: Paul Barnhurst (14:50):
As I listen to you talk, it reminds me of something you and I have said a few times, Glenn, you got to have good data before you expect the LLM to give you good results.
Co-Host: Glenn Hopper (15:00):
So extracting the data has to be, yeah, that's a huge part of it is pulling out the right data to feed these systems so that they can add any value.
Guest: Ali Hussain (15:08):
And to me there's four layers of where the future is going. There's a data layer, which is the most important. Think about the gasoline or the actual vitamins you put in your body, it's the most important, not gasoline in your body, in your car, but it's like the quality of what you're fueling. If that's broken everything downstream, you're going to have a poor drive or your health's going to weaken or whatever it may be. And so that context is the most important thing in the world of ai and that's where hyper specialised tools that know the domain that can pull out contract data, usage data, key fields from your CRM, your data lake, et cetera, that is where you should always buy versus try to build the actual interface is the software I do believe is going to be the most commoditized. And that's why in certain areas I'm just sceptical of all these AI tools that are purely just an operational or software layer if they don't have a data mode that I do think is a race to the bottom, but it is a necessary part because whether you or your accounting firm or your agent are working, you still need systems of record that show the work and show the audit trail.
(16:18):
And I have a hard time believing you're going to build something that's good enough for an auditor, a third party yourself. So I still think you should buy even though it's not going to be as expensive as it was in the past. And then the final piece to your guys' point, the reporting part I do think gets more commoditized because that's so bespoke so long as you have good data coming from these systems. The final part though that we're not yet talking about, but I can bet when we're on this show together a year from now we'll be the specialised agents that are not humans that are also working on top. That is the other area. There's the data part and then the last mile agent. I do believe those agents are going to get hyperspecialized to these roles and that will be another area that will be incredibly hard to build and you'll just buy. So everything in the middle gets commoditized, but the data moat and the actual agents that start to look and feel like team members, they're fine tuning of agentic work and context will get specialised enough where it'll make sense to pay up for because of the quality of work they do and the specialty of accounting workflow they know how to do.
Host: Paul Barnhurst (17:24):
Got it. Speaking of agents, obviously we keep hearing about those. It seems like first it was the LLM, it was prompting, you hear a lot of scripting, but now we're hearing a tonne on agents. So how would you define an AI agent? What do you think they're good at today? What are their limitations and where are we going with them? Ever feel like you go to market teams and finance and speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet QFlow.ai, the strategic finance platform purpose-built to solve the toughest part of planning and analysis of B2B revenue. Q flow quickly integrates key data from your go-to-market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go-to-market stats, makes segmented scenario planning a breeze, and closes the planning loop. Create air-tight alignment, improve decision latency, and ensure accountability across the team.
Guest: Ali Hussain (18:53):
I think there's going to be a lot for a little bit a lot of noise around agents where people will look at something cool than an LM can do it and be like, oh, that's an agent and my bar's much higher for that. That is just a cool workflow or table stakes like automation. And so to me there's a distinction between things that move more automated but are still requiring a human to work on top. That to me is AI automation. It's not necessarily a jet. A AgTech to me are end-to-end workflows that are being fully done independent of a human being involved. The human can give direction, but a human's not coming in and really touching the steering wheel. The agent is getting from X to Y location totally on their own and starting to completely displace entire jobs that people do, whether it's a part of their job all the way up to their entire job.
(19:51):
And so to me, I think the bar for an agent is very high. I think we are still in very nascent days of who's actually accomplished end-to-end agentic outcomes. I think there's a lot of end-to-end AI automation, but an actual ability to say, Hey, I'm not hiring anyone else in billing because now I get a agent from tabs or I don't need a payroll admin anymore. I have an agent from deal. I think we're a little bit out from there, but we're not that far away. And so to your question, Paul, where I think we're headed is in the coming 12 months we're going to see a lot of lower level jobs to be done starting to get displaced. That doesn't mean necessarily you're cutting all your headcount, but you're able to continue to scale and do more with less. So my prediction for the next 12 months and a lot of where our roadmap is headed is what are the entire jobs that can be done that are highly tactical and Billing's a great example.
(20:51):
The amount of people that spend time, a good portion of their time each month building invoices, sending them following up, sharing a PO number, submitting a portal, a W nine, trying to reconcile payments from a bank account. That is an area of where we believe the technology is good enough to fully take that over end to end going into the next 12 months. That is not a hypothetical, that is an inevitable and the quality of the data, the lms, the tooling that vendors like us have built is starting to yield results where you can imagine that happening in a good amount of the market in the decent future. The harder part of agents is what I call the more intimate workflows like replacing what someone like a revenue accountant or an fp and a lead or a strategic finance lead goes that I think is going to take a little bit longer, but that is also an area where agents are going to fully take over. And so my threshold for agent is less about is it eating into ERP spend or software spend or services spend. That to me is AI automation. If you're displacing modules and services from your ERP vendor or your third party admin, that is like automation. But if you're starting to look at your finance team of 15 people and you're saying, look, I don't actually need to hire two more people in billing or in payroll or in revenue accounting or fp and a, that starts to actually, that's where I use the where agents have arrived.
Co-Host: Glenn Hopper (22:23):
Yeah, Paul, I can't believe you asked about agents before I got a chance too. So
Host: Paul Barnhurst (22:29):
I wanted it for once. I didn't want to hear your rant. Kidding.
Co-Host: Glenn Hopper (22:35):
I'm so aligned with you on people were calling everything an agent for a while and I think last time I checked agents were at the absolute peak of the Gartner hype cycle right now and what you were saying about using machine learning at various steps in there, I've fought this and Paul's heard my ran a million times so I won't do it again, but when you call everything an agent, it lessens what a true agent is going to be. I eventually gave up that fight and I would build for clients an agentic workflow that had deterministic steps along the way. You sprinkle some AI where it makes sense, but otherwise it's like a decision tree that it just goes in, it's a deterministic workflow and all that. But I mean I'd be curious. I know some of the coding apps, I don't remember which one on the benchmark had the longest running agent or whatever it might've been cursor, but they've got coding agents that will go out and work for nine hours on something and then come doing truly agentic work as far as I can tell. But I do think it's important to really understand, and this goes back to your probabilistic versus deterministic approach, comment earlier where an agent is still, if it doesn't have those decision gates and those guardrails, it may go off and do a task and do something completely different than it did before. So it's going to be interesting to see how it shakes out and then how we reign in the agents to keep them. I'm going to Nick Bostrom's paperclip thought experiment right now, but it's wild times and in last six months,
Guest: Ali Hussain (24:05):
A hundred percent and that's why we're so fixated on this. I still think the most important part is the data context that has to be perfect. If the agent is working on something that is imperfect that a human would've been able to solve without a system, we have a big problem. And so the data park's huge, but to your point, there are decision rails, there are controls, there are areas that avoid hallucinations or things that could go and send the agent array. But you also, if you think about something like billing, you need preferences stored in memories like this is the way we want you to communicate. This is the tone. Don't go escalate Dunning to Chipotle. It's our top customer. We don't want to piss them off. Turn off Dunning. Those type of controls are incredibly important, but they can be built and I don't think you should build it yourself, but the way, if you now have vendors like tabs that have best in class engineering teams are building off data sets that aren't just yours but off of hundreds if not thousands of companies and getting data on hundreds of thousands of invoices being sent, you can actually pattern match to a pretty good system.
(25:18):
If you think about cars now autonomously driving on complex highways and in cities, it's not that unfathomable that a billing agent can take you from a contract to cash without a human basically touching anything other than once in a while touching the steering wheel to make sure you approve of invoices or who can send the invoice or of a certain size or if you're going to accept a credit card, et cetera. So that's the direction, Glenn, I do think we are headed, but it's going to require really thoughtful specialists like us to get it right versus you going out and on a $50 million business trying to vibe code something. I literally had a client, one of our customers that were about to go onboard. They may went from one system to another, not on the billing side, but on the ERP side and something got triggered and they sent 900 invoices twice to all of their customers on a completely different ERP. And it's just those mistakes can be incredibly costly. Just using a specialist tool and not spending your engineering time on it is incredibly valuable.
Co-Host: Glenn Hopper (26:22):
And maybe this is tangential to that or maybe I'm completely changing the subject now, but I'll be curious to hear your response on this. You mentioned 6 0 6 and I'm thinking about standard whatever, SaaS contracts and all that, but especially with AI being more and more a factor of what people are delivering, the pricing models are changing, so it's usage based, it's seat based and all that. I don't know, I mean I guess ultimately that will impact the kinds of agents you're building, but how are you, or maybe you already have it, but how are you handling increased complexity in contracts and being able to do this pricing? I know that's a big, especially around usage based pricing rather than the seed pricing.
Guest: Ali Hussain (27:06):
A hundred percent. The reason we've done so well in the last 12 to 18 months earlier, Paul asked why I started the company was because I built believe revenue had been left behind for finance operators. And the second reason, and I didn't get to this, Paul, is if you actually look at the 4.7 trillion accounting and finance economy, when you look at the jobs that are being done from a finance composure standpoint, most are tied to the controllership and accounting, whether that at the corporate level doing things under the controller around billing and rev rec, that is typically 60 to 70% of finance teams. But then if you look at the professional service side of a lot of these big top 100 firms, many of the people are in audit revenue related audit revenue compliance, et cetera. So one is it's just a massive labour market, but Glenn, you're going onto something which is like the holy grail of what's going on right now, which is revenue is getting more complicated.
(28:04):
Whether we acknowledge it or not, the prior conventional wisdom is you hire a mature finance team, they come in and they standardise your contracts. They once in a year align on pricing and packaging and they create structure and controls. The last 12 to 18 months have thrown that completely out. Meaning with AI revenue is getting more complicated, but no one actually knows the right answer to it other than we have to move away from flat fee and even seat based pricing on the software side. But even on the non software side, the waste service providers, professional service firms, even hardware manufacturing companies are moving the way they sell products and the terms and all of that. And so our general mission is internally as a company is to make complex revenue simple at scale and the way we handle it's, it goes back to that LM layer.
(28:58):
We're agnostic to your pricing and packaging because we understand and structure it at the moment a contract signed. So we're not relying on a price book, any type of historical structured pricing or knowledge of your business model the same way an accountant or a human reads a contract and then types in what their understanding is. The LM and other deterministic machine learning is highly discerning of that and can support any type of business model. And so if someone asks me What's the most surprising thing about your business, 60 to 70% of our clients now have a usage component to it. And that's not just clients like Cursor and together and others that are like AI companies. A lot of legacy platform companies are now adding some form of usage structure on top. And we are getting incredibly good at not only structuring it, but most companies then feed a usage feed into tabs.
(29:51):
It doesn't have to be API. The LMS really good at capturing unstructured CSV uploads as well from BI tools like Looker and Tableau. We do the math and we do the invoicing. But then most of my roadmap right now is going really deep on the rev rec side because to your point, when you add usage and all these complexity doing your 6 0 6 on usage-based products with tiers and minimums and pre-commitments and performance obligations, it is like a seven 11 freeze times a hundred times over. And I mean people are truly lost. How do you do SSP and revenue allocation at a time of usage and professional services at the same time? And these are all breakthrough macro chaotic moments that are really allowing us to go back and build products that are helping support finance teams going through immense change. Right now,
Host: Paul Barnhurst (30:44):
The pricing landscape is a changing animal. It's why I am glad my business is relatively simple on the pricing. All right, enough of the humour. But yeah, great answer there. And it's fascinating. It'll be really interesting to watch over the next couple of years how this settles because if you're not careful, that pendulum will swing too far in complexity in pricing. There's a balance usage and all these different things are great and getting to value-based pricing, holy grail. At the same time, if it's overly complex, do people want value-based pricing then you never know what the number's going to be every month. So this will be fascinating to watch how it plays out.
Guest: Ali Hussain (31:25):
Yeah, my hot take right now is we're not actually seeing a lot of pure outcomes based pricing yet. Even though people say their value and even in usage, it's not pure risk based. Most people are really moving to some form of tier based pricing and on top of that, but we're seeing a lot of upfront commitments too because especially folks who are moving and selling more AI products to the enterprise, they need some certainty on payment and term. And so I think as much as people are posturing really advanced, complicated usage, we're starting to pattern match a little bit. The problem just becomes the discounting and the things that it requires to get the deal done or highly sales creativity and what the customer's willing to pay. And that's actually where a lot of the complexity still happens today in the market.
Host: Paul Barnhurst (32:15):
So AI hasn't solved the relationship in all the things salespeople do that you're like, I got to figure out now how to manage that
Guest: Ali Hussain (32:23):
Same as maybe one engine sharing of resourcing. They're going out and hiring five more salespeople. So I've yet to see that happen.
Host: Paul Barnhurst (32:30):
Yeah, I know I'm with you. Alright, so we're going to move into, we have a little section we'd like to do at the end. This is our AI section. So here's how it works. We feed an LLM and we use different ones depending on which week it is. I think this one was clawed but I'm not sure. And so we feed it questions we had for the interview your, your LinkedIn profile and tell anything you can find on the web and to come up with a mix of kind of personal, fun and quirky questions. So we never know what we're going to get. We haven't even read the questions. Glenn and I each take a different approach. So my approach is you have two options. There's 25 questions here. We can stay with computer here and let the random number generator pick the number between one and 25 or you can pick a number between one and 25 and we'll put a human in the loop and then we'll ask that question.
Guest: Ali Hussain (33:21):
Alright, I'm a big human in the loop guy still. I still think we need humans in the loop and we will for some time. So let's go lucky 13.
Host: Paul Barnhurst (33:30):
13. I'm not sure if we've ever had that one. Hasn't been many. Alright, here we go. If AI agents were employees, what title and job description would you give the tabs agent and what would its performance review look like after year one?
Guest: Ali Hussain (33:49):
Amazing. I am a big believer that agents must be named like employees, so humans and so I would pick whoever first name@tabs.com is still available. I don't think we yet have a Regina, so let's go regina@tabs.com. And the way I would assess Regina today is pretty similar to how I assess employees at tabs. I put them on a one to four scale with three to four being no one gets a four, so I'd be very surprised, which is a top performer I don't think yet. Regina is at the level where they can be a three either, which is Overperforming, so I probably put them at a two. I don't think they're a one anymore last year. One is typically someone who's on the lower end and struggling, but I think Regina is probably a two. The only thing on top, I want to make sure Regina is not costing me more than an employee, so I'd also track their token utilisation to make sure that the ROI in Regina is good enough so that it's not only better than employee, but I'm also getting some real ROI out it.
Co-Host: Glenn Hopper (34:51):
Great answer. Complete.
Host: Paul Barnhurst (34:53):
I like
Guest: Ali Hussain (34:54):
It.
Host: Paul Barnhurst (34:55):
Good. Right on the plot.
Guest: Ali Hussain (34:56):
I may have already thought about that one. So HUS will be all I on the 13 there.
Co-Host: Glenn Hopper (35:00):
So for my approach to these questions, I feel like I'm just throwing everything over to the AI overlords and instead of just putting a human in the loop at all or even just a random number generator, I ask it, you came up with the questions, which one of these do you like best? Should I ask? So I ran this,
Host: Paul Barnhurst (35:19):
I mean, yes, question 13,
Guest: Ali Hussain (35:21):
I'm just kidding.
Co-Host: Glenn Hopper (35:22):
We have had that happen a couple of times lately. Just a weird glitch on the matrix or something. But now this is number five. I almost want to do a human in the loop and call an audible here. This is an okay question. The AI question is, you worked on Capitol Hill before any of the tech roles, and this is the part I want to change. They say, what's one thing about how DC actually works that you wish more tech founders understood? I want to change the question to be how on earth are we going to make our octogenarian representatives understand the first thing about ai? So harder question, sure, but I'd love to hear your answer on that.
Guest: Ali Hussain (36:03):
This is not what I've thought about other than I think the sooner agents get to also vote may start to create some changes. And so we may be not as far out from that than we realise. I don't know if it's, I wouldn't be surprised. At least in our lifetime, we see some type of bill that passes that says an agent can vote in an election and that may be the biggest trigger of change. And as crazy as that sounds, that's my hot take. Well, love
Host: Paul Barnhurst (36:28):
The answer. Love that you took the time to join us today, Ali, it's been a lot of fun chatting.
Guest: Ali Hussain (36:36):
It's a deep pleasure. Glenn Paul, I know you're both very busy, so thanks for humouring me this afternoon. I'm excited for the next time.
Host: Paul Barnhurst (36:43):
Thanks for listening to the Future Finance Show. And thanks to our sponsor, QFlow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice, and may your robot overlords be with you.