The State of AI-First Services Today
Notes from the field
Over the past months, we’ve explored the rise of AI-first service businesses from several angles: Louis started by laying out why we believe foundation models are now performant enough to support full-stack services and that these businesses could become meaningful in ways traditional software can’t. We then mapped the early landscape and looked at how M&A might accelerate their path to scale.
In these earlier explorations, we argued that their operational playbook(s) and value proposition(s) often diverge meaningfully from traditional software businesses and that their addressable markets could often be multiple times larger.
What wasn’t clear to us was how these businesses could balance automation and service quality, how they could scale operations without linear cost growth, how they could build strong (data) moats, or how they could articulate value beyond cost savings.
After dozens of founder conversations, deep dives into emerging sub-verticals, and some early commercial signals from the market, we now have more evidence to revisit those questions. As part of this, we’re also sharing an updated version of the market map to reflect where we’re seeing the most activity and momentum.
What Mapping Verticals Revealed About Workflow Structure
We began by mapping verticals to understand where AI-first service businesses are most likely to succeed. The initial pattern suggested that the most promising opportunities lie in under-digitized industries with entrenched, low-NPS incumbents like property management. But what’s proven even more important is the shared structure of the workflows these businesses aim to replace.
Whether it’s insurance claims, tax filings, property management, or immigration law, the underlying work is often highly structured and repeatable, driven by documents, rules, and checklists rather than creative problem-solving. Most of it still runs on PDFs, spreadsheets, and legacy systems.
That shared anatomy makes these categories particularly well-suited to being rebuilt from the ground up:
Structured intake and triage: In insurance or property management, each case starts with a flood of semi-structured inputs, e.g., KYC packs, maintenance tickets, scanned forms. AI-native firms use LLM + OCR pipelines to classify and validate this data automatically, routing only edge cases to humans.
Document-heavy review and cross-referencing: Tax firms or claims processing specialists often rely on junior/lower-qualified staff to search through dense statutes, policy docs, or precedent letters. RAG and vector search now surface the relevant clause in seconds.
Repeatable, rules-based decisioning: Whether it’s approving a claim, closing an alert, or filing a compliance opinion, outcomes often follow fixed logic. AI-first teams train policy agents to apply those rules, explain the outcome, and log it immutably.
Low-value manual tasks at scale: Across verticals, teams still spend hours copying data across systems, reconciling ledgers, or filling out forms. End-to-end automation reduces marginal cost per case to near zero and scales throughput without linear hiring.
Massive untapped historical data: Legacy firms sit on decades of returns, claims, filings, and alerts, mostly untouched. AI-native companies structure this data to train vertical-specific models that improve accuracy and create defensibility.
What emerges is a new class of service business: data-in, judgment-out factories. They run on documents, structured data, and rules-based logic. The output is a decision, classification, or filing, increasingly handled by agents instead of human analysts.
Since our original market map, we’ve seen a noticeable uptick in activity across insurance-related services, e.g., Inca or Elysian in claims processing and Flow or Meshed in brokerage. Financial services remain a strong category, with steady expansion across tax, accounting, and compliance. And we’re beginning to see more unique and complex use cases emerge, like Convexia in drug discovery or Operand in management consulting.
Revisiting the Open Questions
Some things weren’t clear early on, i.e., how far automation could go without hurting quality, what scalable delivery would look like, or how these businesses would differentiate beyond price. We’re starting to see more answers take shape.
1/ Getting to the Right Automation Level Without Compromising Service Quality
Across most verticals we’ve looked at, companies are not aiming for full automation from day one. Instead, they’re building workflows where AI handles the bulk of routine tasks and humans remain involved at key points. Specifically in:
Regulatory compliance: In many verticals, there are legal ceilings on automation. German customs brokers are legally required to manually review filings. Property managers must conduct in-person meetings annually. Tax advisors often need certified oversight. In these contexts, human accountability isn’t optional, and will not become so in the near future.
Accuracy assurance: In high-stakes workflows (claims, filings, tax, security), automation errors are costly. Some of the companies we’ve talked to invest heavily in custom verification layers, reviewer training, and tightly controlled workflows. Control sheets, QA loops, and task-specific overrides ensure that speed doesn’t come at the expense of accuracy.
Trust and relationship management: Some industries are still fundamentally human, e.g., brokers, real estate agents, wealth advisors. These customers often care more about trust and service than technical elegance. Integral, an AI-native tax advisory for German SMBs, doesn’t mention AI once on its homepage. Their customers aren’t looking for sophistication; they’re looking for confidence.
We’re particularly excited about companies that manage to productize parts of their service early, without rushing into full automation too soon, or defaulting to stitching together off-the-shelf tools without real leverage. Getting automation right is less about maximizing coverage and more about sequencing it properly, starting with the aspects of the business that provide the biggest operational leverage.
2/ Balancing scalability vs. growth
In AI-first services, growth without automation just means more people. And more people likely lead to margin compression, coordination risk, and brittle operations. We’ve been excited to see some companies starting out by building the systems that allow margins to expand with volume by encoding expertise into infrastructure.
Offdeal calls this “the platform”, which effectively is a unified foundation of structured data, live context, and agent interfaces. Others, like one AI-native TPA in insurance we’ve spoken with, have embedded years of adjuster know-how into systems of data repositories, deterministic agents, and retrieval-augmented models. These setups are designed to decouple growth from operational complexity.
3/ Building Sustainable (Data) Moats
When models and APIs are broadly available, defensibility tends to come from two places: proprietary data and tight operational integration into customer workflows.
On the data side, we’ve seen companies take different approaches:
Some fine-tune models on customer-specific data, e.g., a real estate brokerage might collect location-level sales data from existing clients to improve the prediction quality of future site selection models.
Others gain an edge by understanding structural document patterns across the delivery stack, e.g., an AI credit analyst who can reliably parse and standardize financials across formats, jurisdictions, and edge cases.
A third group builds semi-structured internal knowledge bases that accumulate over time, curated, expanded, and corrected by humans-in-the-loop. These repositories power the agents and create compounding product quality.
On the integration side, defensibility comes from becoming deeply embedded in the customer’s operation. If the system is closely tied to core workflows (i.e. decision-making, compliance, reporting), it becomes harder to rip out. When agents are wired into core processes — like claims adjudication, compliance opinions, or alert triage — they stop being tools and start becoming infrastructure. Replacing them isn’t just a software switch; it requires changing how work gets done.
4/ Building and Articulating Value Propositions Beyond Lower Costs
AI-first service companies often replace existing vendors and tap into budgets that are already being spent. In those cases, price alone rarely wins. What matters is whether the internal ROI from AI-driven delivery translates into better outcomes for the client, measured on the KPIs they already track.
Tenex, for example, competes with legacy managed detection & response providers not just on cost, but on precision and speed, promising higher true positive rates, fewer false positives, and faster detection and response times. In insurance, TPAs are expected to show improvements in loss adjustment expense, lower error rates, and better loss ratios, while also offering more transparency into how decisions are made. Legal tech firms like Covenant position on similar lines: faster turnaround, clear communication, and a fraction of the traditional cost.
These external KPIs are often mirrored internally by a north star metric focused on delivery efficiency, most commonly some form of revenue per employee. It gives an early signal of whether the automation and infrastructure are actually compounding, or whether the business is still scaling like a traditional service firm.
Software vs. AI-First Services: Pick Your Battle
As we are consolidating our findings on this space, we started debating the question of what’s the best way to bring this new technology to market. Through software, or through full-stack services?
The case for full-stack AI services is getting stronger. These businesses expand the market size in two ways. They make previously unattractive verticals viable by absorbing service complexity. And they go after larger budgets by replacing existing service vendors. Their entire organizational structure is built to maximize the ROI of AI implementation. When the offering is differentiated on critical KPIs, there’s often no real PMF risk because the budget already exists. Go-to-market can also be faster. Especially in industries with platform fatigue or slow adoption cycles, positioning as a service provider makes it easier to slot into procurement processes. That said, service businesses come with real tradeoffs. Operational complexity increases quickly. Margins depend heavily on the pace of automation. And scaling the business often requires orchestrating tech, people, and workflows in parallel.
Software remains the cleaner model. Gross margins sit in the 80 to 90 percent range. The cost to serve one more customer is nearly zero. Risk stays with the buyer. And when software is deeply integrated, defensibility compounds through usage, data, and switching costs. But the challenges are real here, too. Customers need to be convinced that black-box AI tools will actually deliver. Many software companies still face long sales cycles because trust and integration need to be earned before a deal is signed. In practice, many buyers still expect to purchase outcomes, not just tools.
We don’t fully agree internally. Louis leans towards software as a more efficient distribution vector. I find myself more drawn to models that start out service-heavy and move gradually toward software margins (maybe Louis is just a boomer, or I am just a GenZ cliche). It’s still early, and we expect the lines between software and services to blur even further. Neither model is perfect. The hard part is knowing which imperfections you’re willing to build around. Pick your battle.