Every day, Munich-based car subscription service FINN delivers between 200 and 300 vehicles to customers across Europe and the US.
Each delivery requires insurance activation, license registration, transport logistics and real-time customer updates — a level of operational complexity that would overwhelm traditional dealerships.
“Just one delivery involves a lot of orchestration,” says FINN cofounder and CTO Andreas Stryz.
That orchestration is handled by AI. Make, the AI-driven automation platform, has become the technology backbone powering FINN’s ability to manage 50,000 cars annually. It’s a glimpse into how car subscription services are using automation to make an inherently complex business model appear seamlessly simple to customers.
FINN’s growth comes at a critical moment for the industry. Research commissioned by vehicle subscription service Pivotal shows 62% of UK motorists faced higher insurance premiums over the past year, while nearly a quarter (22%) had to take time off work to service their vehicle.
Imagine, instead, access to a car without the admin — no insurance renewals, no surprise maintenance bills, no hours lost waiting at the garage.
It’s little wonder that against this backdrop, interest in car subscription services is growing fast; Pivotal projects the UK market alone will expand by over 30% annually through 2032.
The team at FINN, which manages around 50,000 cars annually and generates approximately €250 million in recurring revenue, was charged with figuring out how to keep up.
“We deal with around 30 car brands, plus banks, insurers and OEMs,” says Stryz. Each partnership represents enormous technical complexity that most startups would spend weeks untangling — but FINN was playing a different game. “The goal has always been to integrate any technology within 24 hours.”
And the orchestration of delivering hundreds of cars a day across two continents? “All of that coordination runs through Make,” says Stryz.
Automation as the engine
Make, formerly Integromat, is a Prague-based AI-driven visual automation platform that lets companies connect different systems, from CRM and finance tools to AI models and logistics software, without writing code.
It’s the connective tissue linking the dozens of data sources that power FINN’s day-to-day operations.
The partnership began early. In FINN’s first year, Stryz realized its initial no-code stack — Webflow, Google Sheets, and Zapier — couldn’t handle the company’s growing logic and integration demands.
“Zapier lacked the logic we needed,” he recalls. “Simple things like ‘if B2B customer, no tax; if B2C, add tax.’ Then we switched to Make, and I became a huge fan.”
Today, around 250 FINN employees use Make daily, running roughly 20 million automation operations each day.
“Then, when we entered the US market, we did it entirely through Make — no engineering effort,” says Stryz. “Delivering cars in the US is different: no public transport, gated communities, trucks. We have a saying: as global as possible, as local as necessary.”
“Tools like Make help us build and test operations in uncertain environments.”
Building the future
Sara Maldon, who leads internal automation strategy at Make, says, “Make started as a way to connect systems. But as AI evolved, we saw a shift from connecting tools to building intelligent workflows that can actually execute work.”
Her team at Make, nicknamed the Samurai team, builds and tests automation use cases across departments internally like HR, marketing and operations before sharing them publicly.
“We want companies to move beyond proofs of concept and into production,” she adds. “True automation means you can say, ‘this is what I need,’ and it just happens.”
At FINN, this automation-first approach has helped with everything from driver logistics to HR onboarding.
When a customer gets a speeding ticket, for example, the process of matching the notice to the correct license plate used to be manual and error-prone.
“Now we use AI for detection,” says Stryz. “We integrated GPT-3 through Make within a few hours and improved accuracy significantly.”
This kind of rapid problem-solving matters when customers expect subscription simplicity — one monthly payment, zero hassle. Any friction breaks the promise.
Even the company’s talent acquisition process runs on automation. FINN’s own AI agent, “Ava,” sources candidates on LinkedIn and identifies potential matches for roles, while Make handles the connections between data, applications and workflows.
“Most people think AI’s biggest gains are in customer support,” says Stryz. “For us, it’s talent acquisition and internal operations. Every time we build a new agent, we’re surprised by the results.”
No-code meets AI-native talent
To manage the growing complexity, FINN created a new role: Business Automation and AI Manager, or “BAM.”
These employees, often recent graduates, aren’t traditional engineers but process thinkers fluent in tools like Make and GPT. “They work in every department — growth, operations, HR — and use Make to automate and improve processes,” says Stryz.
Maldon sees this employment model as part of a broader shift in how companies approach automation. “We’re seeing a new generation of AI-native professionals who think in systems,” she says. “They’re not coders, but they understand workflows. Platforms like Make let them turn ideas into production-grade automations.”
That accessibility is part of the company’s core philosophy, says Darin Patterson, Make’s vice president of market strategy. “A few years ago, the mentality was: if we want to grow, let’s just hire more people,” he says. “Now, the new way to scale is to ask what can be automated through ‘agentic’ behaviors — and what still requires human creativity.”
A visual language for complexity
For Make’s enterprise users, the platform’s visual interface is as much about governance as convenience. “At FINN’s scale — around 5,000 active scenarios — visibility is crucial,” says Stryz. “We need to trace dependencies and identify bottlenecks. I actually pushed for that feature.”
Patterson agrees that visibility is one of Make’s underappreciated strengths. “It gives CIOs and IT leaders oversight into how automations are structured and who’s using them,” he says. “That’s key for managing risk, especially now that shadow AI and rogue integrations are becoming concerns for enterprises.”
Maldon adds that Make’s new generation of AI Agents (launched this year) pushes that visibility further.
“We’re moving toward agent-based automation and deeper interconnectivity,” she says. “It’s all about giving people the flexibility to build their own intelligent, context-aware assistants and workflows.”
The road ahead
As FINN continues to expand, automation will remain the humming engine behind its growth. For the car subscription model to truly rival car ownership, this level of automation isn’t optional — it’s essential to make the unit economics work.
But for Stryz, the technology’s appeal isn’t just in efficiency, it’s in potential.
“I’ve worked in tech a long time,” he says. “And I’m convinced we’ll soon see the first billion-dollar company built by a single person using tools like Make and AI.”
Maldon, for her part, sees companies like FINN as proof that automation has graduated from experiment to necessity — and the conversation can now evolve.
“The real challenge now isn’t adopting AI,” she says. “It’s learning how to do it right: how to connect it to your data, your systems and your people.”
