The Digital Executive
Saurabh Chauhan: AI Agents Are Replacing Workflows—Not Workers | Ep 1249
14 May 2026
Chapter 1: What is the main topic discussed in this episode?
Welcome to Corozant Technologies, home of the Digital Executive Podcast. Do you work in emerging tech, working on something innovative, maybe an entrepreneur? Apply to be a guest at www.corozant.com forward slash brand. Welcome to the Digital Executive. Today's guest is Saurabh Chauhan.
Saurabh Chauhan is the founder and CEO of Peakflow, a fast-scaling AI startup that automates complex back-office financial operations for enterprises across APAC and the United States. Under his leadership, Peakflow has secured over 100 enterprise clients, including Fortune 500 companies.
such as Hitachi Construction Machinery, and recently launched Voice AI Agents that quickly rose to the top three products of the day on Product Hunt. The Voice AI Agents are part of Peakflow's flagship 20x AI Agents Orchestrator platform, an open-source agent orchestrator that runs inbounding generation through AI, GEO, generative engine optimization,
Outbound via AI SDR and back office work like month-end close with all the humans in the loop.
Chapter 2: How did Saurabh Chauhan's career experiences shape his approach to AI in finance?
Peakflow positions 20X as the operating system for the era of micro-unicorns. Well, good afternoon, Saurabh. Welcome to the show. Thanks for having me, Brent. Absolutely, my friend. I appreciate it. And you're hailing out of the San Francisco Bay Area in California. I'm in Kansas City. Appreciate you making the time zones, the calendar jumps, et cetera, to get here. Again, thank you.
And Soro, I'm going to jump into your first question. You've had a diverse career spanning McKinsey, Rocket Internet Ventures, and now founding PeakFlow. What key experiences shaped your journey to becoming a leader in AI-driven financial operations?
Sure. So I'll start with my journey at McKinsey first. McKinsey was from 2013 to 15, when I was advising Fortune 500 clients on strategy and operations. So that's really the place where I saw enterprise dysfunction at scale, but from the chair of a consultant. So I think during that time, it was immense exposure to problem statements, but not necessarily
something where we could deliver impact beyond client recommendations so i think at some point i wanted to operate not advice and that's what essentially took me towards rocket internet which was my second scheme so I ran a few ventures for them, the most notable one being Deraz.
Chapter 3: What is the 20X AI Agent Orchestrator and how does it work?
So Deraz is a South Asian e-commerce marketplace that was acquired by Alibaba for $200 million in 2018. So I ran the company in the Sri Lankan market for two years from 2015 to 17, built some, you know, essentially a headcount of five to 60 member team through the revenue 3x year over year. for multiple years.
And honestly, I mean, what I greatly learned over there was managing 60 people was that we were essentially paying for judgment and getting it about 20% of the time. The other 80% went to execution. Lots of manual stuff happens when you're scaling e-commerce marketplaces, like chasing on invoices and reconciling spreadsheets and buying up on customer tickets.
It's the valuable part of every employee was being squeezed into essentially 20% of their day. I took that learning from Rocket, and that's when I met my co-founder, Dimitri. He's a PhD in artificial intelligence. And honestly, we agreed that the next generation of software should not help humans do the work faster. It should do the work and let humans manage it. So that's what took us to Piclo.
We founded it in January 2021, went to Y Combinator in winter 2022, then moved to Google AI Accelerator. $4 million in seed and are now selling 100 plus enterprises across APAC and US, including large publicly listed companies like Hitachi. And yeah, it's exciting to be here and would love to talk more about what we're doing now.
Great. Thank you so much. I appreciate that. I love the backstory. I always do.
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Chapter 4: How do AI agents improve productivity in enterprises?
It's usually my first question. You cut your teeth at McKinsey. A lot of people do. They get a lot of good experience there. And you said you were advising Fortune 500. And as you said, you found a lot of dysfunction in these businesses, which gives you a lot of ideas for your future aspirations and ideas.
The Rocket Internet Ventures was pretty cool, how you grew that into a powerhouse, which eventually was sold. But really starting a startup and moving to Northern California and being accepted into Y Combinator and doing these different things around startups is awesome.
That's always one of those stories that people really want to really sink their teeth in and learn more about the founder, which we're doing today. And Saurabh, in a highly complex financial environment, ROI is critical. How do you ensure AI solutions deliver measurable impact rather than just incremental efficiency gains?
Yeah, absolutely. So I think first off, Brian, I'd probably push back on the way the industry talks about ROI. Most AI software right now is sold at 5% to 15% efficiency gains. And we don't see that as a transformation. That's more or less an incremental optimization.
So I'd be sure we measured in terms of FTEs, the productivity gains in terms of FTE replaced or the cycle days eliminated based on the operation workflows that our clients run. So typically the way it translates into customer group would be 95% accuracy in invoice data extraction, which is essentially better than humans or vendor bill payment time gets cut by 50% or customer payment cycles.
reduced by 15 to 25 days. So we have delivered customer outcomes, which would have been using the same finance team that they currently have in most cases, but the team essentially become agent managers.
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Chapter 5: What measurable impacts do AI solutions deliver beyond efficiency gains?
So that's not really a productivity gain as much as it's sort of creating an entire different category of software. And even inside P2O, Just to sort of give you a reflection, not just in what we do outward, but also inside the company, our 2026 engineering plan had initially called for 15 engineers, or we're operating that on just a team of seven engineers.
The other eight engineers are AI agents and the entire agent infrastructure is AI. Probably, you know, one-tenth of the fraction, one-tenth of the cost of essentially adding those eight engineers. So my test for whether an AI deployment is real is pretty simple. Can we point to a couple of headcount that we did not hire and then the cycle days that did not get extended?
And if the answer is no and our team feels more productive, we essentially go on with that. And it's working really well because we stopped helping humans do the work faster. We essentially give the work to agents and we essentially put humans in management.
So that's where the order of magnitude games come from as far as both internal and external deployments look like when we're serving our clients.
Great. I appreciate that. Pretty cool. And I didn't know, but I like hearing from guests talk about this ROI. You said the industry states it's anywhere from 5% to 15%, but you disagree. And I think that's cool. You measure in FTEs and the number of human hour reductions. So in that measurement, you talked about, do we expand FTEs? or not.
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Chapter 6: How are AI agents transforming traditional organizational structures?
Do we reduce that, those cycle days? And if it's because of those agents, we're able to do that, then we can obviously see an ROI from your perspective. But I really liked that, how you can move the humans into more critical level tasks and then keep the agents working on those things that maybe are repeatable and mundane. So again, I appreciate that.
And Saurabh, Peakflow has introduced the 20X AI agent orchestrator, which promises to deliver 20X productivity gains by turning specialized AI agents into full-time equivalent FTEs for knowledge work.
Can you walk us through what 20X is and how it goes beyond traditional automation tools, especially with applications like AI SDR for sales, AI marketer agents for content creation, AI agents for back office workflows, et cetera?
Sure. So 20X is a self-improving agent orchestrator. It's open sourced on github.com slash peakflow slash 20X. It's MIT licensed and the enterprise version is available on our website peakflow.co. Essentially, the mental model is that 20X is the brain. The models, AI models like Clart, GPT, Gemini, these are the underlying models, are essentially the hands.
And 20X decides what needs to happen, decomposes it into tasks, picks the right model for each, and then surfaces what needs human review. And the three things that essentially separated from, you know, your run of the mill traditional automation would be, number one would be the heartbeats.
So essentially traditional automation waits to be triggered, but as 20X agents are checking proactively, just like a potential employee would be checking the work. So... For example, let's say three invoices are 45 days overdue and I'm just giving you an example of finance use case and or collection drafts are ready for approval.
This would be the sort of things that the agent would be proactively checking in the ERP and emails, whatever are the connected data sources and would be surfacing tasks that require human in the loop or human intervention, but essentially an agent would be performing these checks. And that's essentially one of its superpowers.
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Chapter 7: What advice does Saurabh give to leaders deploying AI agents?
The second is schemes. So schemes would be that every time an agent finishes a task, it updates its own playbook based on what it worked and confidence scores are tracked. So over time, the runbook writes itself. Most AI agents are confident in terms who who don't really learn, but our AI agent essentially gets sharper every week.
Pretty much like it would be the case if you were to hire a full-time employee. So the more they work on specific tasks, the smarter they get. 20x agents operate just like that. And lastly, we're model agnostic. So what that means is we're multi-model by default. I don't know if you...
Recall, but recently in the news, I think Anthropic gave itself like five days notice before cutting cloud access. A lot of companies currently are locked in or single model reliant. When opening, I signed and cleared a GPD 4.0. A lot of companies were impacted. So essentially it removes the single model dependency risk. If you're single model, you can get hit.
So enterprises typically prefer running mission-critical functions like finance automation, but also their go-to-market on not being hostage to one lab's roadmap. So that's really the three outcomes that we love for. And in terms of
Chapter 8: What future trends in AI and enterprise operations should we expect?
The use cases that you mentioned, whether it's AISDR, AI marketer, or AI finance, these are in fact our top three use cases. So the AISDR is our go-to-market champion for outbound sales. The sales just doesn't ride outbound anymore in our client organizations. They manage an SDR agent that runs daily.
The agent will pull signals from the available information for clients from CRM databases or agent databases. It will automatically draft personalized sequences. It would surface prospects that are relevant. And then the judgment is then taken care of by the client. actual human in the loop. That's the same case for AI marketers that are the AI agents for content creation.
So our content lead would manage a marketing agent that runs weekly. The agent would design the briefs, would design the draft, would do A-B testing based on subject lines, and would do the shipping. But the human in the loop would essentially be our content head who would be reviewing all the work and the analytics that are derived from the AI marketer.
And same for our actual product that we deploy for lots of clients, which is our AI finance agent for back office automation. This is what our customers have been running for. Several years now, 100 plus enterprises, large, popular brands like Hitachi. Their finance teams manage agents that do three-way matching, anomaly detection, vendor econ.
And essentially the outcomes are they're able to close their month-end From reduce those times from 10 days all the way down to three days. And I think the proof really is inside our own walls.
My CTO, Dimitri, literally shipped a full enterprise vendor decon product and four days, 10,000 lines of code, everything from document ingestion to data extraction, matching engine, full database schema, web app. And he quite frankly didn't write any of the code. He managed the team of five, six agents that he spun up who did write the code.
So that's essentially what 20S looks like when we love it, not just as a client-facing product, but also something that has created a massive impact in terms of our productivity internally.
That's amazing. And thank you. And I'll just highlight a few things. Obviously, your 20X AI agent orchestrator is a self-improving AI agent that can address most tasks and be able to escalate or elevate complex decisions up to the human level. I thought that was pretty cool.
The skills part, every time an agent finishes a new task, it documents and learns it and adds it to its knowledge base, which obviously sharpens its skills day by day. I like that your model is agnostic or model agnostic, which is, Again, another benefit there.
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