Thomas LJ

Every CIO in the country has been tasked with AI governance, a buzzword that deserves a proper explanation. The below article is a must read. Simply put, if you want to build agents that move the needle for large companies, you have to handle concerns around governance, auditability, and evals, you will fail. There will be 1000x more agents deployed as time goes on, meaning this issue compounds if not handled correctly now. Thomas was an early Dropbox engineer, is top 0.01% in terms of AI understanding and ability, and is now Head of Engineering at Varick. There is no one more qualified to speak on the topic than him!

What does AI for PE firms actually look like? We've been on calls with a handful of PE operating partners over the last 2 weeks. Their #1 issue is that portcos' AI efforts are stuck in the IT org. These companies have scaled and some have moats, but they are not sophisticated from a technology standpoint. Some have large engineering teams, some have a help desk and an outsourced MSP, but regardless, AI efforts get stuck as a side-of-desk activity or with the "3 guys in the closet upstairs." Meanwhile, there is a ton of work that needs to get done. One portfolio company is a distributor where the inside sales team gets orders scribbled on a whiteboard and then re-enters them into the ERP. It takes an hour to enter a 100 item order, and when a contractor sends work to 3 distributors, the first quote back generally gets the work. If you can get the order entered quicker, you get the work. So portfolio companies are struggling with: 1. Hiring: Should we hire someone on the business side to lead AI, or an AI-aware accounting lead? (Good luck hiring these people…) 2. Build vs Buy: There are a ton of "AI for X function" companies out there, and a ton of noise, how do you even begin to navigate? 3. If we build, how do we get the capacity in a portfolio company without issuing an RFP for every use case? (A couple wanted to sign a contract at the fund level, "almost like a captive capability") 4. Are we doing this for labor takeout or to increase the exit multiple? (You can push EBITDA up 5 points, but it doesn't increase the value of the company unless you have a new narrative. "Nobody wants it unless you can show that you have an agentic offering") 5. Should we do outcome-based pricing? (Generally bogs down in contract negotiations - what does it mean to save an hour and is it really worth $17?) 6. Who will do the work? "Everybody has a story to tell, and the sales pitch was great, but the project was terrible. It comes down to who you have walking in the door." 7. What is the business case? We've seen AI briefs that show a function can be 40% automated that were written by someone that never talked to the function. We do this for PE-backed companies every day at Varick. Work with us below:

Here's where AI agents for finance workflows break in large enterprises, and why: 1. More than 25% of invoices a company receives don't have a PO. The coder using Coupa doesn't find a PO to match to, clicks a box saying "there's no PO", and the invoice goes into their ERP to get paid without being checked against the budget. Sometimes this happens for the same vendor for six months in a row. 2. Partner billings are initiated with a ticket that's free-form text. If the business owner doesn't put the GL string on the ticket when he files it, it doesn't get coded properly. One of the tickets said the partner was crediting $3M to a customer, when in fact it was something like $360K. It went to the customer like that. 3. The folks providing data for the close don't know when the deadlines are. Accounts Receivable needed the data by the second business day of the month in order to bill it in that cycle. They didn't get the data until the 4th business day, so Revenue had to accrue it and bill it later. 4. Fixed Asset retirements: there are 500-700 per month. For each one, the team looks up the asset in the ERP, opens up a template, and manually types in three columns of data per asset. If you get one digit wrong, you retire the wrong asset. Their words: "Very manual clicking. Clicking. Nothing to do." 5. Every time a customer set up a new invoice group, the company set up a new customer account in the ERP. They had a CRM integration, but they couldn't turn it on because their customer list was so messed up. They couldn't tell me how many B2B customers they had. 6. The procurement process has 11 segments in the chart of accounts, and each segment sends the request to a different reviewer. Users override the default segments 10-15% of the time, and the last step (done by FP&A) sends the request back to start over. Each request takes two days to more than two weeks to complete. 7. When setting up approval thresholds (say $5,000, $10,000, $100,000, $1M, $10M, $25M), each threshold adds an approver but doesn't remove the previous ones. 8. There are two systems involved in intake and approvals. One routes to legal, sourcing, risk management, etc. The other (the ERP) is used for budget approval and the PO. Their words: "Users are always confused... what's a source system." 9. Contract terms changed from what was quoted. Legal redlined the contract, which removed the metadata that would have flagged the non-standard discount the customer negotiated. It passed Finance's review and Sales ops review, because they thought it was the standard discount. It was only caught by the system when it calculated the discount. 10. Project accruals: each month end, they email every project manager asking if they've done any work over $5,000 that hasn't been invoiced yet. Then they chase down the replies and calculate the accrual (e.g. the project is 60% complete but only 50% billed, so they need to accrue 10%) in Excel. 11. Contracts are billed on a schedule, not based on what was delivered. For example, an ad deal will bill the same amount each month. If they don't deliver what was sold, they have to get the customer to sign new paperwork. 12. Pass-through costs: the customer contract and the PO to the vendor are in two different parts of the ERP. They do have a wash account to record it, but whether anyone is reconciling it, "I doubt it." 13. Order entry is still done from a photo or PDF of the customer's order. It takes an hour to type in an order with 100 lines, and the customer submitted the same request to three competitors at the same time. 14. There's no tool for coding transactions to the GL, so they don't know how similar transactions have been coded in the past. One of their business lines issues credit memos, which have a whole other list of GL accounts, which almost doubles the size of the chart of accounts. 15. There's no API for downloading bank recs or vendor portals, so somebody does it by hand every month. If you'd like to fix any of these in your existing ERP, that's what we do at Varick. Work with us below:

Hiring: 1 Engineers: to join our Platform team, built the agent operating system that powers the world’s largest companies. 2 FDEs and 2 FDSs to join our Delta team, interface with C-Suite executives, redesign entire departments, and build agents to deliver millions in value. Guarantee this will be the most fun company you will ever join, and you’ll learn a ton. Everyone wears multiple hats, everyone comes in early and stays late, and we’re growing 100% month over month. Up to 250k base plus Equity. DMs open.

This article articulates the issue with enterprise AI so clearly: the main reason AI pilots fail isn't even because of the AI. Most enterprises have piloted 3 or more AI projects in the last 12 months. Yet, you won't find any of them on their P&L. Why? - They built the AI model based on the SOP, but real processes have exceptions that can be 40% of the volume. - They thought a quicker W-9 review would improve vendor onboarding. It didn't. That process still takes 22 days. - There wasn't a clear owner for the AI agent 7 months in, after the innovation budget ran out and the consultants left. Fantastic article by the former COO of Citadel Securities in the EMEA region. If you've piloted AI projects that never made it to your P&L, read this before piloting another.

We're still in the early innings of AI transformation. Varick estimates the market for AI transformation will grow 100x over the next 3 years. Finance is one of the best places to start, for a few reasons: - Most of the time is spent waiting. One invoice exception takes 25 minutes of work, but 11 days to clear. - Finance leaves a better data trail than any other function. Every invoice, approval, and journal entry is timestamped somewhere, even if half of it lives in Excel. - Much of the work is rule-based. You can't afford for agents to hallucinate in the finance function, and by incorporating heavy determinism, you de-risk the agents. In the article below, our COO Eyad breaks down the exact playbook we've used to roll out AI in finance at some of the largest companies in the world. If you're working on agents in finance, or you're an FDE, this playbook is a gold mine.

What has to go right for AI to actually show up as positive in your P&L: - Having the right sponsors: someone from the C suite, with a budget that they can spend and, very importantly, the involvement of the owners of the processes that you're going to be automating. And, also, the early involvement of key stakeholders, like legal, information security, IT, and finance. - Really understanding the as-is processes that you're going to be automating: this requires process mining of the systems of record, and an understanding of the detail of each process, who's involved, all of the variations, and the volumes and the cycle times. - Understanding the baseline of the process and the metric that you're going to measure to understand how much value you're creating: are you looking at time saved, cost saved, revenue increased after spending the tokens, or risk reduced, and agreeing the key KPIs that you're going to measure. - Re-engineering the processes, as you automate them. Usually, you'll end up with about five steps, of which four of them will be automated using AI. And use plain code wherever plain code works. - Building AI agents that work with all of the systems that you've already got in place, like Salesforce, NetSuite, SAP, ServiceNow, Dynamics, et cetera, so that you don't have to move to a new system, or have people log into a new system. And having one architecture layer underneath all of those AI agents. - Deciding which decisions that the AI agent can make versus where you need human input, and making that decision based on both the risk and the cycle time to make the decision. The agent proposes, a person makes the call, and every action is reversible and traceable. - Running the AI agents in shadow mode, in live with your volumes, and comparing their output to the people doing the process now. And not paying out anything they produce until you're absolutely sure that the AI agents are doing what they're supposed to be doing. - Identifying the 5% to 10% of your people who are already using AI, and getting them to be the AI ambassadors and evangelists, and then training all of your people on how they work with the AI agents, and explaining how their jobs will change. - Having a plan for 7 months from now, where you own all of the AI agents that have been developed, all of the data, all of the evaluations, all of the logs, they're all in your cloud, and they're built to be agnostic to any particular AI model. We do all of these at Varick. Partner with us below:

C Suite executives around America are eagerly awaiting Opus 6 and GPT 7 so that they may finally one-shot the agent-ification of their company. By then it will be too late. A fascinating analysis by Daniel, former COO of Citadel Securities EMEA.

"AI and jobs is a complex equation". No kidding. Great prescriptive analysis for enterprises, based on real client engagements.