The Next Great AI Companies May Look Like Services Companies

Broadly speaking, software is credited for streamlining messy industries by making them vastly more legible and manageable. And in many cases, this reputation is well deserved. After all, software holds the power to systemize record-keeping, digitize workflows, and scale processes far more efficiently than paper, spreadsheets, and phone calls ever could.

But until recently, software’s reach stopped short at the edge of the work itself. While a bookkeeping platform may have handily organized the books, someone still had to close them. Sure, insurance systems could manage policies, but someone still had to marshal data, negotiate coverage, and walk customers through the process. And although logistics software tracked shipments, humans still had to coordinate carriers, resolve exceptions, and troubleshoot problems.

That all began changing with the next generation of AI, whose most consequential innovation isn’t merely improving software in the old sense, but its ability to compress that coordination layer and build better service models on top of it. Within this paradigm shift, the pertinent question isn’t whether AI hastens existing workflows; it’s whether an AI-native operator can outperform the incumbent that bolts AI onto the old workflow framework.

An AI-native services company is an enterprise that sells a completed service or outcome to the customer, while commandeering AI as a core part of the machinery that delivers it. The customer is not primarily buying a software seat. They’re buying insurance, bookkeeping, benefits access, home maintenance, compliance, and other finished jobs. Underneath that experience--software, models, agents, and human operators all collaborate to deliver desired outcomes.

This distinction matters. These businesses may seem more like brokers, administrators, agencies, or managed service providers than traditional SaaS companies. But their economics can fundamentally differ from the services businesses that came before them because AI sits inside the production function.

Consequently, the question is no longer simply whether AI can make an existing workflow 20 or 30 percent faster. It’s whether a company built around AI from day one can deliver the service faster, cheaper, and more reliably than an incumbent attempting to layer AI onto a labor model and organizational structure built decades ago.

SMBs: ideal customers

Small and medium-sized businesses (SMBs) are compelling customers for this model. Large enterprises that can readily afford specialized teams are in position to buy software and install staff to operate it, enabling procurement departments, benefits desks, finance teams, compliance pros and other operational personnel. Contrarily, most SMBs don’t have deep enough pockets for these functions.

The owner of a 30-person company doesn’t necessarily want better insurance software; they want their company to be insured. They don’t want another bookkeeping dashboard; they want accurate books at the end of the month. They don’t want a procurement workflow; they seek someone to source vendors and negotiate prices.

This is why SMBs already spend extravagantly on services, outsourcing the functions where they lack expertise or manpower to build an internal team. That makes SMBs an unusually attractive go-to-market wedge for AI-native services companies. Instead of convincing the customer to create a new software budget and change the way they work, the startup can often attack an existing services budget and offer a better version of something the customer already pays for.

The pitch shifts from, “Here is software that will make your employee more productive,” to “Give us the job.”

That is a much more consequential shift than adding AI features to SaaS.

Start with a painful wedge

The startups that gain the most traction don’t attempt to rebuild operations--whole cloth. Instead, they remedy a single broken wedge that is sufficiently painful, urgent or expensive, so that customers are willing to embrace behavioral change.

Corgi is a prime example. Their wedge is startup insurance, a category where coverage can sit directly on the critical path of fundraising, board formation, or closing customers. Self-described as AI-native, Corgi is a full-stack insurance platform and carrier for startups, not simply an “insurance software provider.” Consequently, Corgi wraps software and AI around a cumbersome time-sensitive service to make the customer experience faster and more reliable.

That wedge-first approach is important. Rather than replace an entire category in one fell swoop on day one, successful companies cherry pick narrow jobs painful enough to demand behavioral change, thereby paving the way for a more thoughtful macro approach.

Where I am most interested

The most compelling opportunities share a few characteristics: large existing services spend, fragmented providers, repetitive coordination work, expensive human labor, and an end customer who cares much more about the outcome than about how the work gets done.

SMB back-office services may be the clearest example. Insurance, bookkeeping, tax, payroll, compliance, procurement, and other administrative functions consume meaningful time and money inside small businesses, despite rarely being strategic differentiators. These are categories where the customer often wants the responsibility taken off their plate entirely.

Healthcare administration has similar characteristics. Much of the healthcare system is not constrained by a lack of software, but by the enormous coordination required to navigate institutions, paperwork, eligibility requirements, payers, and government programs.

Mindset Care, a tech platform that helps users access financial resources, exemplifies the consumer side of healthcare administration. By deftly handling SSI and SSDI applications at no upfront cost, it wraps personalized, end-to-end support around a paperwork-heavy workflow that has traditionally been slow, manual, and difficult to navigate.

In logistics and physical operations, the underlying work remains fragmented across phone calls, emails, spreadsheets, vendors, and operators. AI does not need to physically move a pallet or repair a machine to transform these businesses. There is enormous value in handing everything around physical actions like quoting, routing, communication, documentation, exception management, and follow-up.

The more contrarian extension of this thesis shows up on the consumer side, in markets where supply and customer experience hiccups. Home services are a clear example, where the challenge lies in sourcing trustworthy, highly responsive people, coordinating the job, and managing follow-up protocols. This isn’t about whether a human shows up, it’s whether someone can build an AI-native home services company where the homeowner buys reliability and convenience from the platform, rather than vetting a different individual provider every time.

Casa is a recent example of this. Rather than framing it as another point solution, Forerunner touted Casa as a coordinated service layer around the home, built on a digital home record, a concierge layer, and a managed vendor network underneath. In that model, the company sits above fragmented supply and reorganizes the experience around the end customer.

The economics will not look like SaaS at first

One implication is that these businesses may look strange when evaluated through a traditional SaaS lens. Revenue may initially resemble services revenue. Gross margins may be lower. Humans may remain deeply involved in the delivery process. The key question is what happens to the economics as the company scales.

In a traditional services business, growing revenue usually requires adding labor proportional to the amount of work performed. In an AI native services business, the goal is to break that relationship. As the system improves, a single operator may manage more customers. More interactions can be automated. Exceptions can be flagged earlier. Pricing and routing can improve. Response times can fall. Work that once required specialized employees may increasingly be handled by software, with humans reserved for the highest value or most ambiguous decisions.

The venture scale opportunity therefore is not necessarily a services business with SaaS margins on day one. It is a business where the marginal cost of delivering the service keeps declining as more of the production process moves into software.

Software is moving inside the company

That is why the best AI-native services companies may not look like classic SaaS businesses from the outside. Instead, they resemble operators, brokers, administrators, or managed service providers. But the software is still there, moving inside the company, behind the scenes. The customer is buying the finished work, while software, models, agents, and human labor are orchestrated underneath.

That also means the moat may not initially look like classic SaaS. It may show up in routing, pricing, utilization, service quality, response times, trust, and supply-side density. In some categories, this comes from owning the relationship with demand. In others, it comes from owning more of the workflow itself. Either way, the defensibility compounds as the system learns.

Underneath these startups are software companies in a different form. The customer will not be buying the software. They will be buying the work.