Debunking the Most Common Myths I Hear About Services Businesses

July 2, 2024

Silicon Valley dogma says to build product businesses and avoid service business models. 

For a while, I embraced this thinking: after closing my chapter leading the People team at Datavant, I  wanted my next step to be building an important, ambitious company. I naturally assumed this would be another SaaS business. But after carefully considering my options, I realized I could take a huge swing by joining a dev shop.

The number one question I get from candidates is some version of “a professional services business…but why?” 

It turns out…I think Silicon Valley’s dogma is wrong: you can build an ambitious, giant, scalable business with a services business model. Below I bust the most common myths around professional services businesses.

Myth 1: The most valuable problems are always solvable with a SaaS solution

Over the last decade, SaaS dogma has reigned supreme in Silicon Valley and for good reason. Many aspects of the SaaS business model – high margins, predictable revenue, scalability – are fundamentals of any good business.

That said, as the saying goes, “to a hammer, everything looks like a nail.” From a SaaS mindset, every problem looks like a productize-able solution. Instead of starting with "how do I productize ___ (fill in the blank)?", we should start with "what's the best way to solve the customer's problem?". 

At Fractional AI, we believe some of the most transformative impacts of genAI will come from automating complex workflows within large companies – these are the customer problems we exist to solve. 

Solving these problems – take automating a call center – requires customization based on the company’s data and desired outcomes, integration with dozens of other systems and workflows, adaptation to the company’s cost and quality needs, and iterative learning over time.  It’s hard to build a nuanced, reliable genAI solution for just one company, let alone an “out-of-the-box” offering for hundreds of companies. 

Rather than defaulting to the SaaS “hammer” to solve this specific problem set, we lean into exceptional engineering talent and a bespoke approach – blending end-to-end custom builds with “off-the-shelf” products to meet the customer’s needs.

Myth 2: Recurring revenue is the most important metric 

In the realm of SaaS businesses, recurring revenue is often the holy grail. You sell a subscription and generate a steady stream of repeatable income that promises stability and growth. 

The reality is that optimizing for “recurring revenue” in SaaS is overly dogmatic. Having strong long-term value from customers with minimal customer acquisition costs is a relevant goal from first principles, but recurring revenue isn’t the only way to get there; Google, Facebook, Apple, and Amazon don’t primarily rely on recurring revenue in their core businesses.

At Fractional AI, we are obsessed with creating long-term value for our customers – and to keep our customers coming back (our core cultural value is ‘we overdeliver’ for this very reason!). That said, there are many ways to do this beyond the typical SaaS subscription – think customers buying subsequent projects, pricing that aligns incentives (e.g., revenue shares on products we build for our customers), or high switching costs (it’s easier to hire the dev shop that already knows your product and code base). 

We’re not the first to think of it this way – Accenture had 300 “diamond clients”  in 2023 and 267 in 2022. It’s generally believed Diamond Clients pay Accenture over $100 million per year. Is Diamond Customer revenue subscription revenue in the SaaS sense of the word? No. Predictable revenue? Yes.

Myth 3: Services businesses only scale with people and are, therefore, inherently low margin

Part of the romanticizing of SaaS is scalability – you invest upfront in R&D, build the software, and then sell that software again and again with minimal variable cost. This generates a high gross margin (though the net margin might be less glowing after taking into account all that R&D spend). 

All businesses – SaaS or services – should keep an eye to scalability, or “how do I serve as many customers as possible as efficiently as possible.”  It’s true that in many legacy professional services businesses – think law firms – there is a linear relationship between people and revenue (to bill more hours, you need more people, and thus revenue growth is tied to headcount growth). 

This is a fair critique but it’s anchored on yesterday’s assumptions on pricing and resources in professional services. Our business model at Fractional AI doesn’t abide by these assumptions.  

First, we find high leverage projects and participate in the upside.

  • In terms of pricing, we have a project based fee, but the real value comes from shared success incentives (e.g., if we automate a workflow leading to $10 million of savings, we get a percentage of these savings as revenue). 
  • This moves us away from a ‘cost plus’ pricing model with compressed margins to a value based pricing model. 

Second, we build best practices that will help us deliver faster.

  • Scaling will require hiring, but much of our scaling will come from internal products, know-how, and libraries. 
  • Automating a call center the first time takes a team of engineers; automating a call center the 20th time (with the playbooks from the 1-19th times) takes far fewer resources as we benefit from economies of scale and scope. 
  • There’s a reason why McKinsey can do competitive market analyses faster than many companies can internally; they have the templates, know-how from thousands of market analyses, and relationships to deliver efficiently. Or look at Pivotal Labs - a software consultancy; their internal products became so valuable, they launched them as external products. 

Third, we use AI to enable every team member to deliver maximum value.

  • The economic impact one engineer can have with AI is larger than any time in history. 
  • In 2022, Netflix generated over $2.4 million of revenue per employee. We’re building a team with the requisite AI tooling to surpass that (one of our core values is ‘we overuse AI’ for this reason!). With this math, scaling a Services Business to $1 billion annual revenue requires hundreds, not thousands of people.

This combination - i) value based pricing, ii) internal products and know-how, iii) leveraging AI in all that we do - enables us to maintain high margins and not solely scale with headcount.

Myth 4: There are no moats in services businesses 

In any business - SaaS or services, you want to be thinking about the differentiators that protect your business from competitors.  Moats can range from intellectual property (think patents in Pharmaceutical companies), to network effects (think Uber or LinkedIn), to proprietary data assets (think Google), to ecosystem and integrations (think Salesforce app store) and even stickiness (think of the high switching costs of replacing your ERP). 

The reality is truly good moats are hard to come by whether you’re a SaaS or services business. Many SaaS businesses have weak moats: as a consumer, nothing really is preventing me from switching from to Asana or Trello. Similarly, there are some slight switching costs, but moving from Lever to Greenhouse or Gusto to Rippling is relatively easy. 

The best professional services businesses create strong moats in a few key ways - 

  • Talent network effect. They hire and train top talent, many of whom go on to become future clients. This creates a virtuous cycle - the company attracts top talent, clients want experts solving their problems, and so on. 
  • Knowledge network effects. Unmatched access to how dozens of clients are approaching similar problems. 
  • Proprietary tools and technologies.  Internal products, libraries, and frameworks based on the knowledge network effects. 

Myth 5: If I join a Services business, my equity will be worthless

Many of the candidate questions, deep down, are coming from the place of: “if I take this risk, will it be worth it?”. I’m glad candidates are reflecting on this question - joining an early stage company is a big decision for candidates and their families. 

The first underlying piece of this myth is that services businesses aren’t valuable. The numbers point to a different story here (see Accenture’s $200 billion market cap).

The other underlying piece of this myth gets to employees' ability to participate in the upside. Traditional professional services businesses often operate as partnerships, with only partners holding equity, and few of the iconic silicon valley legends have a professional services model (though I’d argue Palantir has leaned services heavy). 

Just because equity has been consolidated within professional services businesses in the past, doesn’t mean it has to be in the future. At Fractional AI, everyone is an owner, and counter to SaaS businesses which require large up-front investments, professional services businesses have a rapid path to profitability – opening the door to profit sharing much sooner.

Cutting through the false dichotomy

Ultimately, as I tell candidates, it’s not a question of whether a SaaS business model or Services business model is inherently better or worse in a vacuum, but which model best positions the team to solve the problem.

Specifically in this genAI wave, automating complex workflows requires top-notch engineering talent. Most companies lack said talent, and we’re building an exceptional professional services business to bridge this gap.

Fractional AI is the dev shop for difficult applications of genAI. Ready to solve the world’s most valuable problems using AI (and build a huge services business to boot)? We’re hiring.


Annie Powers is the VP of People and Operations at Fractional AI. Before Fractional, she led the People Team at Datavant and was an Economic Consultant at Analysis Group.

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