A First Landscape of AI-first Service Businesses
Some key considerations, a few examples, and a market map
Since the advent of LLMs and new AI capabilities, the venture ecosystem has started paying attention to a new category of businesses: AI-first Service Businesses. Louis wrote our preliminary thoughts on it early last year. Over the past few weeks, in order to refine our thesis, we’ve mapped and analyzed over 500 companies operating at the intersection of AI and services.
Below, we share some key insights and a market map.
A Framework for Categorizing AI-first Companies
Before diving into the specifics of AI-first service businesses, it’s helpful to step back and consider the broader landscape of AI-first companies. After carefully reviewing hundreds of businesses, we’ve observed that these businesses can be grouped into three distinct categories, each with unique characteristics and implications for scalability, differentiation, and value capture.
Agentic Software (Autopilot): Fully autonomous AI systems capable of managing workflows from start to finish without human intervention, such as ScienceMachine (AI data analyst for drug discovery), Sphinx (agents for KYC & AML tasks), or Conveo (AI-moderated voice & video interviews). These solutions excel in highly structured and repeatable processes such as automated data entry or monitoring. While they promise SaaS-like or superior margins (they don’t have any “humans in the loop”), they can face significant barriers to adoption, as customers often demand extensive pilots to build trust and validate reliability before fully outsourcing a process to an AI.
AI-First Workflow Automation (Copilot): Tools that augment, rather than replace, human workers by integrating seamlessly into existing workflows, e.g., Harvey (legal AI-copilot), Ascend (automation of KYC/KYB workflows), or Console (IT support co-pilot). These solutions are ideal for complex tasks where human judgment is essential, but automation can benefit portions of the process. With their familiar SaaS pricing models, such as subscriptions or usage-based fees, these have similar scalability levels and business models as the previous generation of SaaS software.
AI-First Service Businesses (Service Model): These companies aim to disrupt service markets, often presenting themselves as traditional service providers but natively adopting AI to streamline labor-intensive processes while increasing their margins. By focusing on services rather than software, they can capture more substantial portions of customer budgets (e.g., recruitment costs or compliance spending). While they may not achieve the high margins of pure SaaS models, their hybrid strategies enable them to penetrate industries that have historically resisted technological disruption.
Let’s now focus on the last category.
The Unique Value Proposition of AI-First Service Businesses
We won’t go as deep as Louis’ post from last year here. In short, we believe these businesses are interesting for two main reasons.
One, unlike traditional SaaS businesses that go after IT budgets, AI-first service businesses go after labor/people budgets. They deliver complete operational outcomes, capturing a share of spending that can be substantially larger — potentially encompassing entire operational budgets rather than just an IT line item. Imagine a typical bookkeeping scenario: while tools like QuickBooks cost $50–$150 monthly, the bulk of the expense — $500–$2,000 — goes to labor, i.e., the accountant. Companies like Pilot flip this equation: they provide an end-to-end bookkeeping service, combining AI automation with human oversight. They charge $349 for pre-revenue startups up to $999+ per month for growing companies, automating core tasks, and capturing a significantly larger share of the total spend.
Two, beyond increasing ACVs by delivering operational outcomes, AI-native services could allow startups to turn historically low-margin or labor-intensive service verticals into high-margin opportunities for software-first startups. Exemplarily, traditional European property management businesses, such as Vonovia or Deutsche Wohnen, rely heavily on manual processes and fragmented software. They manage routine tasks like tenant communication, maintenance scheduling, utility billing, rent collection, and administrative duties through labor-intensive efforts, resulting in high costs and limited scalability. In contrast, AI-first startups like Buena, Ralph, Hallo Theo and Arbio operate as genuine service companies, blending advanced automation with human oversight and regular in-person support.
These businesses automate tasks such as tenant requests, rent collection, contract processing, maintenance coordination, and financial management. Buena leverages automation to streamline new leases, tenant care, utility billing, and accounting, significantly cutting administrative burdens. Ralph enhances the client experience through automated customer service, real-time document management, and efficient handling of maintenance requests and property inspections, all backed by a dedicated contact person — see a screenshot of their FAQ below.
By automating these manual processes while maintaining human involvement, AI-first companies significantly reduce operational costs, increase service efficiency, and transform traditionally low-margin property management into high-margin, scalable business opportunities suitable for venture investment.
Our Market Map
After mapping 500 businesses during our research, several industries stood out as prime candidates for disruption by AI-first service businesses. These verticals share common traits: high labor intensity, structured workflows, and mounting pressures to reduce costs, improve efficiency, or deliver outcomes faster.
Legal Services: AI can automate time-consuming tasks like drafting contracts and reviewing evidence during the discovery phase, where documents, emails, and records are collected. This frees lawyers to focus on strategic work. Human oversight ensures reliability in complex or high-stakes cases. Legal Process Outsourcing (LPO) has existed for years, but AI now enhances it by increasing speed and accuracy. EvenUp in personal injury cases and Atticus in employment and disability law are two interesting examples.
Financial Services: Processes such as bookkeeping, due diligence, and compliance are well-suited for AI automation as they are repetitive and rule-based. Automating these tasks reduces errors, saves time, and enhances accuracy. Companies like Pilot illustrate this approach by providing SMEs and startups with comprehensive AI-driven financial services, integrating bookkeeping, tax preparation, CFO support, and R&D tax credit management into one cohesive back-office platform.
Human Resources: AI can streamline repetitive tasks like candidate screening, scheduling interviews, and matching talent to job openings. This frees HR teams to focus on fostering workplace culture and addressing strategic initiatives. Mercor, which quickly scaled to >$70M in ARR, is a prime example in this category.
Logistics: In logistics, AI is transforming areas such as claims processing for lost or damaged shipments (Claimit), automating B2B invoice recovery (Respaid), and even building AI-driven logistics carriers (Cartage) that manage entire shipping processes from start to finish.
Insurance: In insurance claim processing, companies like ClaimSorted, Reserv, and Strala use AI-driven Third-Party Administrator (TPA) solutions (TPAs manage insurance claims processing on behalf of insurers) to transform a traditionally labor-intensive, low-margin industry into a scalable market. AI automates processes such as First-Notice-of-Loss, claims routing, and triaging, significantly reducing manual workloads. Real-time analytics accelerate fraud detection and reduce risk, while end-to-end claims visibility enhances insurer decision-making.
IT Services: AI is revolutionizing IT services by automating complex tasks such as legacy system modernization, custom software integration, and cybersecurity threat detection. This transformation enables IT professionals to focus on strategic initiatives, enhancing overall efficiency and service quality. For instance, Mechanical Orchard specializes in modernizing critical legacy applications without disrupting their current operations.
Compliance and Cybersecurity: Companies like Oneleet, Bastion, and Delve leverage AI to automate compliance and cybersecurity tasks, including threat detection, continuous monitoring, vulnerability assessments, automated security audits, and regulatory adherence.
Property Management: Lastly, as discussed above, AI-first property management companies like Buena, Ralph and Hallo Theo automate key workflows while maintaining human oversight.
All of the above are just observations we’ve gotten mapping and speaking to companies in this space, which we find incredibly exciting.
If you want to be added to the map, chat to us about this nascent field, and help us develop this thesis, please write us here!