You Don't Get Re-Rated “Just for AI”
I discussed the “AI re-rating” thesis from my prior post with a few investors. One of the most skeptical pieces of feedback that really stuck with me was along the lines of, “Customers don’t pay for AI, and the market doesn’t re-rate you for shipping agents. It re-rates you when the P&L moves.”
That is absolutely right. And it highlighted to me that I need to ground my original thesis in operational reality and specificity.
So this is the practical version of the thesis. If you are sitting on a traditional SaaS asset and trying to decide what to do next, you do not have one strategy. You have three. Pick the one that matches reality, then execute it like you mean it.
To be clear upfront, the "Re-Rating" isn't about the technology. It's about the financial profile the technology unlocks through real operational transformation across both product and GTM.
This is the same point I made in my previous post, How to Monetize Your AI Roadmap. Most AI ships as a capability or a feature. It increases usage and performs well in demos, but it does not change how customers buy, renew, or expand. In PE terms, that is the gap between product progress and multiple movements. Investors re-rate you when AI moves you up the monetization ladder, from feature to product or digital labor, and the economics show up in retention, pricing, and cost-to-serve.
If you are sitting on a traditional SaaS asset (CRM, ERP, HRIS) and wondering how the current AI technology change will impact it, you don’t have a single strategy to consider. You have at least three. And you need to pick the one that matches your asset's reality.
Why This Matters Now
I still believe investors can't afford to just wait things out. The median hold period for PE-backed software companies reached 5.8 years in 2025, while distributions to LPs dropped from ~29% of NAV in 2014-2017 to ~11% in 2024. Meanwhile, 30,000+ unsold PE-owned portfolio companies sit in the global backlog, and the S&P 500 has outperformed PE over 1-, 3-, 5-, and 10-year periods.
The AI transformation question isn't about whether it should be pursued. It's about which assets justify the bet, and what proof points are required in 12 months.
The Three Paths (Pick One)
1. The Cash Compounder (AI as a Shield)
The Asset Profile: Sticky system of record, low growth (5-10%), high EBITDA margins, loyal but static customer base.
The Strategy: Do not try to pivot to a "Growth AI" narrative. You will burn cash and confuse the market. Instead, use AI defensively to protect the renewal.
The Playbook:
Defensive Moat: Here, AI is a shield, not a sword. You deploy it to prevent a competitor from stealing your "System of Record" status, not necessarily to charge more. It protects the renewal, ensuring the cash flow remains durable for the eventual exit.
Efficiency Features: Ship AI features that make the current workflow 10% faster.
Pricing: Bundle it for free or a nominal fee. Do not try to monetize it as a new line item.
The Goal: Maintain Net Revenue Retention (NRR). If AI stops churn, the multiple holds.
Illustrative Example: A vertical SaaS system of record that does not need a TAM expansion narrative, but does need to make renewals feel safer in an AI-disrupted market.
2. The Consolidator (AI as Synergy)
The Asset Profile: Mid-market player, fragmented competitive landscape, strong balance sheet.
The Strategy: Use AI to execute a Roll-Up strategy faster and at a lower cost than before.
The Playbook:
Data Gravity: Acquire point solutions not just for their ARR, but to aggregate their proprietary data into your central model.
OpEx Reduction: The play here isn't just product innovation; it's using AI to strip out G&A costs across the acquired entities. For example, deploying a unified AI support layer to replace three disparate support teams feeds the "Cost Synergy" thesis investors love.
The Goal: Multiple arbitrage. Buy smaller, non-AI assets at 3x, integrate their data into your AI platform, and trade the combined entity at 6x because of the "Platform Premium."
Illustrative example: A roll-up where you standardize the data model and admin layer quickly, then use a single governance and search experience as the glue.
3. The Transformer (AI as Growth)
This is the one everyone wants to be. It is a change in who pays, what they pay for, and how the economics scale. It moves you to Levels 3 and 4 on the monetization ladder from my previous post.
The Asset Profile: High-value data, direct access to revenue-generating workflows, scalable tech stack.
The Strategy: This is the only path to a true "Re-Rating" (Multiple Expansion). You are fundamentally changing what you sell and how you sell it.
The Playbook:
New Pricing Unit: Move from "Seats" to "Outcomes" (e.g., from charging per marketer to charging per campaign launched).
Digital Labor: AI doesn't just "help" the user; it does the work.
The Goal: Accelerate growth to 30%+. The market re-rates you because you are now tapping into a larger Service Addressable Market (SAM), and replacing human labor budget, rather than just tapping into the same budget.
Example: Workday introduced Flex Credits in September 2025 for Illuminate agents (Performance, Recruiting, Financial Close). Instead of bundling AI for free, they introduced consumption-based pricing where credits are consumed when agents complete tasks. Workday can now expand beyond HR/Finance seat budgets to include operational workflow budgets. Early traction shows customers increasing total contract value by 15-25% when adopting agentic workflows, even with flat user counts.
The underwriting questions that matter most
These are the six questions that require clear answers before underwriting any AI transformation.
1. What job are customers paying us to do, and what gets measurably better?
Not “more intelligent.” What improves, by how much, and how will the customer notice?
2. Who pays, and is the money incremental?
Name the buyer, the budget line, and whether the spend is additive or cannibalized. Ideally, you want to see not just the ability to tap into a greater share of the same budget, but also adjacent budgets.
3. What is the pricing unit that makes the value feel fair?
If the answer is still “seats,” assume resistance to paying materially more.
4. What happens to gross margin and cost to serve?
If AI raises COGS and does not raise willingness to pay, you are compressing the multiple.
5. What has to be true in 90 and 180 days?
If you cannot define traction milestones, you cannot manage execution.
6. What are we willing to stop doing to fund this?
If nothing gets deprioritized, the “transformation” will live in decks and pilots
If you are doing M&A, use the 180-day shipping test
AI capability acquisitions can help accelerate transformation, but only if you buy something you can integrate into a paid workflow quickly.
The one test I would focus on is whether you can ship a paid, adopted workflow within 180 days post-close that changes expansion behavior.
If the honest answer is no, you are buying optionality and hoping the market credits it.
A practical post-close plan should be plain:
Day 30: integration scope locked, data model mapping defined, packaging decided
Day 90: workflow in beta in real accounts, usage measured
Day 180: GA, sales enablement shipped, first paid adoption, and renewals influenced
The Litmus Test for Re-Rating
To know if your asset can actually re-rate, ask these three due diligence questions:
Do we own the Outcome? (Can we charge for the result, or just the tool?)
Is the Gross Margin durable? Warning: Many "Agentic" revenue lines look like SaaS top-line but behave like Services bottom-line (50% gross margins). If you replace 90% gross margin seat revenue with 50% gross margin compute-heavy revenue, your multiple will compress even if revenue grows.
Is the Data Proprietary? (Do we have data that OpenAI/Anthropic cannot scrape from the public web?)
If you can answer “yes” to all three, transformation is plausible. If you cannot, do not force it. Be excellent in the lane you are actually in.
Closing
I hope this grounds the concept of the original post in greater operating reality. It also ties back to my previous post that covered the AI monetization ladder. Most PE-owned SaaS assets should not try to become “AI-first companies.”
What they should do is pick the lane that matches their truth:
protect the core and compound cash,
consolidate the category and simplify the platform,
or fund a real wedge that changes who pays and how the economics scale.
AI can be part of any of those strategies. It is not the strategy by itself.
If you’re building, buying, or operating in this space, I’d love to compare notes.
You can reach me at faraaz@inorganicedge.com or on LinkedIn.
