How MGT Insurance's accounting team got out of the "doing" A three-person accounting team at MGT Insurance built over 50 agentic operations, with no engineering involvement. By letting AI handle the repetitive data entry and coordination, the team saved 30 hours a month and transformed their department into a strategic command center. MGT Insurance is the first AI-native neo-insurer for small commercial property and casualty, built from the ground up for independent agents. As a full-stack carrier, MGT owns its product, platform, and risk, writing Business Owner's Policies and other coverages through independent agency partners across the country. Learn more at mgtinsurance.com. When Gregory Schroeder joined MGT Insurance a year and a half ago, he had previously spent 9 years at an insurtech startup that didn't yet allow their accounting team to use AI. While MGT encouraged experimentation with AI, early attempts to bring it to the accounting process fell flat. At first, the team tried using standard chatbots. "ChatGPT answered questions. It didn't do accounting," Gregory explains. "Give it an Excel file and the output was unusable. Yes, it's AI, but it's what I consider ineffective AI. You're not revolutionizing accounting processes. You're bypassing some research." Upgrading to Cowork and a Claude plugin for Excel made the work feel a bit more real. The Excel output was better, though still hard to checkpoint and iterate with. Having Claude control his Chrome browser allowed AI to draft journal entries in NetSuite, but it took 10 minutes to do a single one rather than a bulk CSV upload doing many entries in one minute. Gregory described this as moving from "ineffective AI" to "hand-holding with AI". It felt a little like magic because AI was actually doing the work, but every task was still initiated by hand and took far longer than the manual route. As he put it, they still weren't operating at the "AI agent level" that he saw his company's engineers using. The breakthrough happened when they stopped trying to make an AI click through a web browser and instead connected Malleable directly to NetSuite. Gregory didn't just want AI to automate clicking buttons, he wanted to shift his team's focus from repetitive work in spreadsheets to leveraging their domain expertise to add strategic value to the business. As a result, Gregory needed a way to teach AI exactly what he wanted it to do, and how his team should be involved in the agentic operation. This is when Gregory met Malleable's team. He was skeptical. He remembered thinking "every tool promises to revolutionize how we work, but it only helps the people who live in GitHub or Snowflake. NetSuite never fits. But I'll give them a few hours to see if it's any different." He started with NetSuite incrementally. First, a very simple form that his team could visit to enter a basic journal entry. An agent then drafted it in the background, the form asked for his approval, and then it finalized it. This proved the capability was there and Gregory gained confidence in how he could oversee agents. "The first run was just two journal entries. It didn't save time, but it proved the hypothesis," Gregory says. Once they knew it worked, they scaled it up. "That was the turning point, when we got an agentic operation with NetSuite," Gregory says. "It took a workbook, ran calculations, and through the right due diligence and approval, got it into NetSuite. My team was then hooked." By August, an agentic operation was routinely preparing and posting entries as large as 1000 lines. When Gregory had the first form that could post to NetSuite, he brought it to his team's weekly virtual happy hour. It's a standing meeting that's deliberately not about daily operations - the team uses it to trade what they're learning about AI with each other. He showed them how he could send an email and see it in NetSuite two minutes later. He described it to them as "you tell AI what you want it to do, it draws boxes and lines to show you what it will do, and then it does it." It felt approachable, rather than abstract, and soon the team was building their own workflows too. The strategy started simple: try spending 15 minutes building an operation that saves 30 seconds a day, then keep going. It was like a muscle they were building together. Some example wins: * The team used to spend Sunday mornings downloading AP aging reports and manually working through overdue bills. Now, a daily operation runs at 10am to triage the ten oldest bills from Ramp to the right people on Slack. * The team built watchdogs to automatically flag duplicate payments and prepaid expenses. * Agentic operations run throughout the day, checking Ramp and NetSuite 50 times a day so Gregory's team can focus on other things. Once they had small things running on autopilot, the bigger targets became obvious. In August, the team set up an operation to automate claim checks from a daily bank feed and recorded almost 400 checks their first week. Next, they updated almost 800 vendor-subsidiary records in NetSuite without any manual data entry. Accounting answers to auditors and regulators. Gregory's biggest fear was a "rogue run", AI thinking it books a single $1,000 entry and accidentally getting 10,000 of them. Malleable's handoff system prevents this by keeping humans firmly in the loop. The AI prepares the work, but it is unable to interact with NetSuite until a human accountant reviews it in a secure form. In August alone, the AI paused for a human decision 400 times. Furthermore, while some tools scope access based on users or workflows, granular permission levels are locked based on the stage and progression of a workflow. This means one part of a single workflow may be able to find overdue bills in Ramp, but afterwards that same workflow cannot use Ramp in any other way. MGT is steadily moving toward what Gregory describes as an accounting command center. His mandate to the team is simple: get out of the doing, and do the thinking. Instead of manual data entry, AI agents now pull data autonomously, run the initial close steps, and stop at their checkpoints. The accounting team walks in, reviews the work, and spends the rest of the day on actual insights to improve the business. "We save 30 hours a month, which by itself is dramatic for a small team," Gregory says. "What's more important is that we now have the space to use our actual experience and give leadership guidance on what needs to happen. This lets us finally spend our time on the strategy we were actually hired to do." "I'm not technical. I don't think of myself as a software builder; I'm an accountant," he adds. "But this is the first time in my career that I can take a vision for how I wish my job worked, and how I wish my team could collaborate, and we can make it happen."

Co-CEO Graham Topol named a 2026 Insurance Business America Rising Star. MGT Insurance is very proud to share that its Co-Founder and Co-CEO, Graham Topol, has been named a 2026 Rising Star by Insurance Business America - the publication's annual recognition of the top insurance professionals under 40 in the USA. The Rising Stars list highlights leaders who are shaping the future of the industry, and this year's honorees were recognized at Insurance Fest on July 16, 2026, in Santa Monica. Graham co-founded MGT to build the first AI-native neo-insurer for small commercial property and casualty, built from the ground up for independent agents. Under his and Co-CEO Michael Topol's leadership, MGT has grown from an idea into a full-stack carrier serving agents across the country - pairing modern underwriting technology with the human expertise that placing small commercial risks demands.