Enterprise software buyers and investors still care about multiples. In artificial intelligence (AI)-enabled Software as a Service (SaaS), the valuation conversation is increasingly shifting toward a different question: What portion of value is already proven, and what portion depends on execution that has not happened yet?
That shift is showing up in how deals are priced, how diligence is run, and how sellers can strengthen their negotiating position. Recent market signals point to an inflection where organizations are moving beyond early experimentation toward scaled AI integration, disciplined iteration, and measurable outcomes. At the same time, many deal teams are navigating a reality where transactions often hinge on realistic expectations and flexible structures that help bridge gaps between seller optimism and buyer underwriting.
Put those together, and a practical theme emerges: AI can increase the spread of potential outcomes, so more transactions are being built around milestones that convert promise into proof. In other words, headline multiples still matter, but in AI-enabled SaaS, buyers often want evidence that value creation is observable, repeatable, and defensible.
Why Does AI Push Value Discussions Beyond the Headline Multiple?
AI has become a key lever for value creation in enterprise SaaS. It can influence product differentiation, customer retention, margin profile, and growth efficiency. But the same forces that make AI attractive can also make it harder to underwrite with confidence, especially when AI value depends on adoption inside real workflows and not just feature availability.
As AI deployment scales, buyers may come to accept that AI can support improved operating leverage. They may also be reluctant to pay fully upfront for outcomes that depend on factors such as:
- Customer adoption inside real workflows (not just “AI is turned on”)
- Data readiness and model governance (reliability, monitoring, and change control)
- Integration complexity across systems, vendors, and security boundaries
- Cybersecurity and privacy implications, as AI can increase access to sensitive data and expand integration surfaces
For many enterprise buyers, cybersecurity and trust considerations are often connected directly to value, risk, and timeline. For a deeper look at how AI and integration can reshape the cybersecurity posture of SaaS environments, our FORsights™ article, “AI’s Impact on SaaS: Adoption, Integration, & Cybersecurity,” provides perspective on the topic.
Bottom line: Multiples still matter, but AI tends to put more weight on execution evidence. The more “in-flight” the value, the more the conversation shifts from a single price number to a set of measurable milestones.
“In AI-enabled SaaS, the question is shifting from the headline multiple to what’s proven versus what’s still in flight.”
From Multiples to Milestones: What Are Buyers Really Buying in AI-Enabled SaaS?
A milestone-based valuation conversation is a pragmatic response to a world where AI can materially improve performance, but outcomes vary widely based on execution. In practice, “milestones” typically fall into four milestone categories.
1) Product & Workflow Milestones
Buyers tend to differentiate between “AI is on the road map” and “AI is delivered in a way customers actively use.” In valuations, that distinction can matter as much as the feature itself.
Examples include:
- AI capability released into production for target segments
- Adoption and sustained usage within priority workflows
- Measurable time-to-value improvements across cohorts
2) Data & Governance Milestones
As AI moves into broader deployment, stakeholders tend to pay closer attention to the quality and controls behind AI outputs. The goal is disciplined operations and defensible decision making, not perfection.
Examples include:
- Clear data lineage and data quality improvements
- Model monitoring, change control, and documented review processes
- Third-party dependencies identified and managed
3) Cyber & Trust Milestones
As AI expands access to data and increases integration points, security postures can shift quickly. Buyers may look for signs that security practices have adapted to AI-enabled operating realities.
Examples include:
- Security controls tailored to AI use cases and new data pathways
- Strengthened identity and access governance across integrations
- Reduced high-severity findings over time, with clear remediation discipline
4) Unit Economics Milestones
AI value is often most persuasive when it shows up in unit economics, not just product messaging. Buyers tend to look for financial signs that AI is improving performance without creating a cost-to-serve problem.
Examples include:
- Lower cost to serve through automation and workflow redesign
- Gross margin expansion tied to operational improvements
- Better retention and expansion in AI-enabled customer segments
What this means for valuation: Milestone framing often helps separate messaging from performance. It can also make growth expectations easier to diligence because the “why” is tied to operational evidence and measurable progress.
Why Are Contingent Structures Showing Up More in AI-Driven Deals?
When milestones matter, deal structure becomes a practical way to “pay for proof.” That is one reason contingent consideration and other flexible mechanisms are often used to bridge valuation gaps and allocate risk more precisely than a single “price at close” number can. The use of creative deal structures has increased to handle the combined optimism and uncertainty inherent in valuations for AI-enabled SaaS companies.
Here’s how common structures can map to AI-related uncertainty.
- Earn-outs: Earn-outs often appear when a portion of the valuation thesis depends on growth, retention, or margin expansion that has not fully materialized. In AI-enabled SaaS, earn-outs may be tied to adoption, retention, or usage outcomes that reflect whether AI is truly landing with customers. Practical note: Earn-outs tend to work better when metrics are clear, objective, and measurable. The more subjective the metric or not fully within the seller’s control, the more friction can show up later, leading to potential earn-out disputes.
- Structured equity: Structured equity can help price upside tied to AI-enabled value creation while providing downside protection if execution lags. It can also create a cleaner path when both sides agree on the long-term potential but differ on the near-term confidence.
- Rollover or founder equity: When founders and management remain central to integration, adoption, and go-to-market performance, rollover equity can align both sides around delivering milestones that support the valuation thesis over time.
- Seller notes: Seller notes can reduce cash at close while providing time for AI-related improvements to show up in results. They can also offer a pragmatic bridge when servicing capital is a consideration.
What Is the Credibility Challenge?
AI can make a growth story more compelling, but an anecdotal story rarely holds up in diligence. A milestone approach works best when management teams translate AI claims into documented assumptions backed by evidence, metrics, and controls.
One way to accomplish this is to build an “AI value evidence pack” that is diligence-friendly and easy for a buyer to test and evaluate.
A Practical AI Value Evidence Pack
1. Assumptions register
Capture each AI-related assumption, what must be true for it to hold, how it will be measured, and what evidence exists today.
2. Proven versus in-flight versus aspirational value drivers
Separate what is already observed from what is supported by pilots and what is longer-term. This supports a “pay for proof” conversation without undermining credibility.
3. Cohort and adoption analytics
Show how AI-enabled customers behave versus non-AI cohorts. Buyers often find this more persuasive than top-line projections because it demonstrates whether AI is changing customer outcomes.
4. Milestone definitions written the way a buyer would test them
If contingent consideration is likely, define metrics carefully and include example calculations. Clarity reduces disputes and speeds diligence.
5. Scenario ranges, not one perfect forecast
Build base, downside, and upside scenarios tied to adoption and implementation timing. This can help teams discuss risk in a disciplined way.
What Does This Mean for Founders, Executives, & Deal Teams?
In today’s SaaS deal environment, AI can support valuation. It can also introduce a larger confidence gap that buyers will try to price through diligence and structure. The teams that are often better positioned are not always the ones with the boldest AI narrative. They are often the ones that can show:
- AI is integrated into workflows and adopted by customers at scale.
- Data governance and security practices support reliable deployment.
- Value creation is measurable and documented in a way that holds up in diligence and deal terms.
Actions to Consider Now
- Translate AI claims into a milestone map: Identify the few outcomes that matter most, such as adoption, retention, margin, and risk posture, and define how you will measure them.
- Build an assumptions register and evidence pack: Distinguish between observed results and forward expectations so the story is testable.
- Prepare for structure early: If part of the value depends on AI execution, expect earn-outs or other contingent mechanisms and plan metric definitions accordingly.
- Treat cybersecurity as part of value creation: As AI scales, protecting proprietary and private data becomes more central to exit readiness.
- Pressure-test scenarios: Build base and downside cases tied to implementation timing and adoption so you can discuss risk without losing credibility.
How Forvis Mazars Can Help
AI is changing what gets rewarded in diligence and valuation conversations. The market is placing more weight on execution, measurable outcomes, and the operational realities that make those outcomes repeatable. As a result, more deals are shifting from “multiples alone” toward milestone-supported pricing, often reinforced through structures designed to allocate uncertainty more explicitly.
If you are preparing for a transaction, recapitalization, or valuation event, it may help to align your AI story with evidence and milestone definitions that buyers tend to test. To discuss valuation readiness, transaction support, or how AI-related milestones can be documented in a diligence-friendly way, connect with professionals from Transaction Advisory and Valuation at Forvis Mazars today.