An interesting article discussing AI disruption and how SaaS companies can thrive or fail as a result of this disruption. Highly relevant to recent discussions we have had in this forum. Perhaps useful in identifying interesting investments as AI continues to evolve.
(Article discovered via Bert Hochfeld in his Ticker Target newsletter.)
I found the article interesting as it dives into a phenomena I have been observing and researching for the past two months: In spite of market wide fears that AI will replace software, some SaaS companies are seeing accelerating growth and decreasing margins over the past two quarters as they implement various forms of AI tools.
My own research has led me to a firm conclusion that equating “AI disruption” with “AI will replace software” is incredibly short sighted and likely creating an investment opportunity by way of compressing valuations for high growth companies. I have been looking closely at the comments about AI strategy in SaaS company earnings calls to gain insights in this direction. This article perhaps provides a framework for evaluating some of those comments to help identify interesting investments.
A brief summary:
The article focuses on two aspects of how a company functions in order to determine AI disruption: How much of a user’s actions can be automated, and how deeply can an AI penetrate into the SaaS workflows. Below is my own summary table of their comments on how these factors interact. Vastly oversimplified, see the article for details.
| Category | User Automation vs AI Penetration | Suggested Action |
|---|---|---|
| AI enhances SaaS | low user automation, low AI penetration | Cost savings from using AI |
| Spending compresses | low user automation, high AI penetration | Very difficult to compete against AI, focus on AI agent development and partner integrations |
| AI outshines SaaS | high user automation, low AI penetration | Goldmine for incumbents by building AI features on top of existing functionality and data |
| AI cannibalizes SaaS | high user automation, high AI penetration | Battleground between incumbents and new AI competitors, with incumbants having a first-mover advantage |
Towards the end of the article is their suggestion for what a SaaS might do to thrive through this AI disruption:
Strategic priorities for SaaS leaders
Will AI and agents disrupt SaaS? Yes. In some cases, that disruption will grow the market; in others, it will commoditize the market. In some cases, the disruption will favor incumbents; in other cases, it will favor new entrants. Disruption is mandatory, but obsolescence is optional. What can SaaS executives do to navigate this opportunity?
- Make AI central to your roadmap. Look for the key jobs that your software helps users accomplish, and deploy AI to automate and speed them up. Take a customer-centric view: Identify repeatable tasks that smart agents can handle, and implement those before your customers look elsewhere. This could mean integrating off-the-shelf models or training your own model with your data. Turn your product into a “do it for me” experience, and help customers see the ROI. Embed AI deeply, stay at the center of the workflow, and deliver more value.
- Turn unique data into your edge. Your data is your moat. While models such as GPT-4o are everywhere, the real value lies in the proprietary data you own—usage patterns, domain-specific content, and transaction history, for example. Double down on capturing and using this data to deliver results no outsider can match. And protect it. If you connect with other AI platforms, make sure your terms stop them from learning from your data and cutting you out. The aim: Become the best source of truth for a key process or data set. Workday’s positioning as a secure hub for managing both human and AI workflows is a good model.
- Shape investment and competitive plans across the four strategic scenarios: core strongholds in which AI enhances SaaS, open doors in which spending compresses, gold mines in which AI outshines SaaS, and battlegrounds in which AI cannibalizes SaaS.
- Decide your strategy for addressing the semantic gap for your industry.
- Get your house in order: Standardize how you define key objects within your own platform. This sets the foundation to either join or lead the next generation of industry-wide agent platforms.
- Open source early, selectively: Publish schemas in which you already lead—as ServiceTitan and Guidewire do. Doing nothing cedes definition power to others; giving away too much puts competitors on a fast track. In standards wars, early movers with a practical solution often win.
- Make it hard to copy: Build unique constraints—for instance, approval flows, state transitions, and compliance rules—right into your data model. Any external agent should have to validate through your system of record.
- Rally the ecosystem: Standards stick when vendors, customers, and cloud platforms align. Bring the group together, shape the agenda, and offer real code to become the default leader.
- Rethink pricing for an AI-first world. Seat-based pricing may not fit when AI is doing the work. If an agent replaces a human task, customers will expect to pay based on outcomes, not log-ons. Start experimenting with pricing tied to results: tasks completed, tickets resolved, AI outputs generated. Leaders, such as Intercom and Salesforce, are already shifting in this direction. The fundamental shift is to stop charging for access and start charging for work done. Stay flexible as you learn what your customers value most.
- Build AI fluency across the business. AI needs to be a core capability, not a side project. That means helping your teams understand what AI can and can’t do, hiring or training the right talent (from machine learning engineers to prompt designers), and building a culture that’s excited about innovation. Everyone—from product to sales—should be able to explain how your AI features work and what value they deliver. And that fluency should extend to customers, too. Help them understand and get the most out of what you’ve built. In the end, your organization should be as comfortable using AI as a new hire is with a browser.