AI-Multiple Expansion and Operating Margin Gain (AI-ME & AI-OMG)

Managed Service-as-Software: A Financial and Valuation Framework Addendum

Quick Recap:

In last month’s Founder Catalyst edition, I introduced the concept of the Managed Service-as-Software startup where AI-driven service-oriented startups build their companies according to a new business model blueprint. This requires a fundamental shift in mindset for startups to use AI, rather than to sell AI. Initially labor-intensive with low gross margins, these startups gradually shift to higher SaaS-like gross margins through automation and AI. As described in my previous post, this model had already been proven before in the managed cybersecurity space where companies like Expel and Arctic Wolf started as lower margin managed security service providers and evolved into higher margin software based security platforms using automation.

In that article, I tried to emphasize the need for an initial labor-first approach coupled with robust AI tooling, and operational monitoring to achieve SaaS-like efficiency and profitability while providing high quality service delivery that is human augmented. This follow up piece dives into the financial and valuation implications of this shift, specifically focusing on AI-Operating Margin Gain and AI-Margin Expansion.

AI-Operating Margin Gain

AI-Operating Margin Gain refers to the gap between consulting gross margins and software gross margins, that is exploitable using AI, agents and automation. Traditional consulting businesses typically operate with gross margins in the mid-30% range due to their labor-intensive nature since the service is delivered via consultants. In contrast, software businesses often achieve gross margins around ~70% because of their lower delivery costs, usually COGS in the form of cloud hosting costs. This significant disparity makes consulting companies operationally less attractive. AI-driven M-SaS startups have the potential to bridge this gap by gradually automating their services, thus reducing labor costs and increasing gross margins over time. By leveraging robust AI tools and maintaining operational intelligence, these startups can achieve SaaS-like efficiency and profitability, ultimately driving higher valuations.

AI-Margin Expansion

AI-Margin Expansion (AI-ME) is defined by the gap in valuation multiples between consulting businesses and software businesses that can is exploitable using AI, agents and automation. Consulting companies typically trade at enterprise value to revenue (EV/Rev) multiples of 2-3x, while software companies often achieve multiples of 8-12x, and sometimes as high as 25-30x during bull markets. This difference is primarily due to the scalability and high margins of software businesses, which public market investors find more appealing. By transforming into M-SaS businesses, AI-driven startups can position themselves to capture these higher multiples while starting with a more labor intensive approach in the beginning. This involves not only delivering software but also providing a comprehensive service, thereby raising their gross margin profile and attracting better valuations long term. The old venture capital adage of “don’t do services” is maybe from a bygone time, where the gap between software and consulting businesses was significantly larger, like 10-15x, than it is currently, like 4-5x. For the best performing hybrid software / services companies like Palantir for example the multiple is almost the same or even better than any other software business. With the advancements of AI, the convergence between software and services will likely speed up even more. 

For Managed-Service-as-Software Startups

For startups looking to build Managed Service-as-Software businesses, the implications of AI-OMG and AI-ME might be profound. Starting with low or even negative gross margins is acceptable due to initial investments in labor and compute. However, the journey from 0% to 70% gross margins must be meticulously managed with both a focus on operational intelligence and patience as the process can span several years and the business matures. This fundamental shift from a traditional software business model to an M-SaS model requires a holistic approach to managing engineering, product, sales, marketing, and operations. By offsetting labor costs with GPU costs and leveraging AI to enhance service delivery, startups can significantly improve their margins and achieve software valuations.

As always, if you are thinking about or building in this space please reach out to me or join our Founder Catalyst community!