Evaluate each software company by asking whether AI can replace the paid job its product performs, what keeps customers from switching, whether the company can defend or improve its revenue and margins as AI changes pricing, and whether the stock price already reflects those risks. An AI feature announcement is not proof of a moat, and a lower valuation multiple is not, by itself, proof of a bargain.
Start with the customer’s paid job, not the company’s AI pitch
Describe what the customer pays the software vendor to get done. Identify the user, the buyer, how often the job occurs, the result the customer needs, and what the customer would use instead. Then separate the product’s core function from its interface, add-on features, and marketing claims.
Ask how much of the product an AI agent could replace
Substitution risk is more concerning when the paid task is easy to reproduce and the customer can adopt an alternative without losing important data, permissions, audit trails, integrations, or accountability. It may be lower when software coordinates a critical workflow, controls access to essential information, or supports work where errors have serious operational, financial, or regulatory consequences. These are questions to investigate, not guarantees that a product is safe.
Do not assume that a system of record or transaction engine is protected just because customers rely on it: test whether a competitor or agent could perform the same job and whether customers could migrate. Conversely, a product with an exposed interface may still be valuable if it sits inside a workflow that is costly or risky to replace.
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PwC argues that platforms built around essential workflows, unique data, and deep industry expertise may strengthen their position, while surface-level features and seat-based growth models face sharper pressure as AI lowers barriers and accelerates competition. That distinction gives investors a useful starting hypothesis; it does not establish the durability of any individual company. A new AI assistant or product launch is evidence of an investment, not proof of customer demand, retention, or pricing power.
Test the moat with customer and product evidence
Strategy& identifies several characteristics to examine when assessing AI defensibility. Treat each as a diligence prompt and verify whether customers depend on it in practice:
- Mission-critical workflow embeddedness: Is the product used in a necessary daily process, and what breaks if it is removed?
- Data control and rights: Does the vendor have access to data that is distinctive and legally usable for its product, or can customers and competitors obtain comparable data elsewhere?
- Vertical expertise: Does the product encode industry-specific knowledge that is difficult to reproduce, or is the expertise mainly in general-purpose features?
- Regulated or compliance-heavy processes: Do permissions, auditability, compliance obligations, or accountability make substitution harder? Establish what actually applies to the customer and product rather than treating regulation as an automatic shield.
- Services or hardware intrinsic to the offer: Is implementation, support, or connected hardware integral to the result, or could customers use the software independently?
Evidence can include workflow frequency, integration depth, customer switching experience, implementation requirements, renewal behavior, and customer references. Also ask whether customers truly rely on these advantages and whether a rival can access similar data, expertise, or distribution. A theoretical switching cost is not persuasive if customers can leave without meaningful disruption.
Separate defending revenue from monetizing AI
A company can retain its place in a customer workflow without charging more for AI. It might also grow the work it serves while earning less per seat. Those are different investment cases: one is defense against displacement; the other depends on delivering enough additional value to support revenue and acceptable economics.
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Check whether customers are paying for better outcomes
Look for disclosed AI adoption and paid usage, customer return on investment, renewal effects, and changes in contract or pricing structure. Distinguish recurring paid use from trials, bundled features, or management’s stated ambitions. If the vendor says AI lets it sell an outcome rather than access to a tool, ask what measurable customer result supports that claim and whether reported revenue reflects it.
Include the cost of delivering AI
Examine inference expense and any reported effect on gross margin, support costs, or delivery efficiency. AI may increase the value a customer receives while also raising the vendor’s costs. A credible thesis needs evidence that the company can preserve or improve customer outcomes and sustain its economics—not just that it has added AI to the product.
PwC describes a shift from selling access to a tool toward delivering outcomes. For an investor, that is a proposition to test against customer behavior and reported financial results, not a conclusion to assume.
Assess the underlying software business as well as its AI position
AI exposure does not make recurring-revenue fundamentals irrelevant. Review the company’s growth, customer retention, profitability, and cash generation alongside its product position. Software Equity Group’s summary of buyer priorities includes ARR scale and growth, gross and net retention, profitability, and Rule of 40; it also reports scrutiny of gross margin, customer acquisition cost (CAC) payback, and annual contract value (ACV).
- ARR growth and scale: Is recurring revenue expanding, and how much of the growth comes from existing customers, new customers, pricing, or acquisitions?
- Gross and net retention: Are customers leaving or reducing spend, and are retained customers expanding their purchases? Read the measures together rather than relying on a single growth rate.
- Customer concentration: Could a small number of customers materially affect reported growth or renewals?
- Gross margin and sales efficiency: Are delivery costs, CAC payback, and ACV consistent with the company’s growth strategy? Track whether AI changes those economics.
- Profitability and cash flow: Compare reported profits with cash generation and the reinvestment needed to sustain the business.
- Stock-based compensation and dilution: Consider how employee compensation affects shareholder ownership over time, not just the company’s reported operating result.
Rule of 40 is a combined growth-and-profitability heuristic, not a substitute for examining cash conversion, accounting quality, or the durability of growth. A company can meet a broad benchmark and still have weak retention, costly customer acquisition, or substantial dilution.
Compare the company’s value with the stock’s price
A strong business can be a poor investment if the market price assumes too much growth or too little risk. A falling multiple can reflect either a temporary increase in the risk premium or lasting deterioration in the business. Assess the company and the stock separately.
Choose a valuation method and make the assumptions visible
A scenario-based discounted cash flow (DCF) or comparable-company analysis can help, provided the assumptions are explicit. For a DCF, state assumptions for growth, margins, reinvestment, dilution, and discount rate. For peer comparisons, match companies as closely as possible on growth, profitability, capital intensity, customer mix, and risk; a broad software average can hide material differences.
Build downside, base, and upside cases around specific drivers: customer losses, seat compression, competitive repricing, AI compute expense, successful AI monetization, and operating leverage. The purpose is not to predict one exact outcome but to see which assumptions drive the valuation and whether the current price leaves room for error.
Use sector figures as context, not as a buy signal
The following published figures are historical snapshots, not current quotes or estimates of fair value. The category medians illustrate why a single software-sector multiple is a poor substitute for relevant peers.
| Measure | Reported figure | Source and qualification |
|---|---|---|
| EV / one-year-forward sales for a Bessemer Venture Partners index Rule-of-40 company | 9.0x to 5.6x, a 40% fall over the prior 12 months | PwC Strategy&, March 16, 2026. The article describes a broad reset in risk premiums; this is not an intrinsic-value estimate. |
| Median EBITDA margin for the SEG SaaS Index | 9.1% in 2025 | Software Equity Group, 2026. |
| Median EV / TTM revenue for the SEG SaaS Index | 4.8x at 4Q25 | Software Equity Group, 2026; historical index median, not a current trading quote. |
| ERP & Supply Chain, SEG SaaS Index | 6.7x EV / TTM revenue | Software Equity Group, 2026; category median at 4Q25. |
| Security, SEG SaaS Index | 6.3x EV / TTM revenue | Software Equity Group, 2026; category median at 4Q25. |
| Financial Applications, SEG SaaS Index | 5.3x EV / TTM revenue | Software Equity Group, 2026; category median at 4Q25. |
| Vertically Focused, SEG SaaS Index | 4.6x EV / TTM revenue | Software Equity Group, 2026; category median at 4Q25. |
| Analytics & Data Management, SEG SaaS Index | 4.5x EV / TTM revenue | Software Equity Group, 2026; category median at 4Q25. |
The index figures do not make companies in a category interchangeable: growth, margins, customer mix, and AI exposure can differ substantially. Use them to frame questions about dispersion, then compare the target with appropriate businesses and your own assumptions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Read market sentiment and deal activity in context
Software Equity Group reported 2,698 SaaS M&A transactions in 2025, approximately 58% of total software M&A activity, and said AI-referenced targets represented approximately 72% of SaaS deals. “AI-referenced” includes deal materials mentioning AI capabilities, integrations, or relevance to data infrastructure; it does not mean that 72% of the targets were pure-play AI companies. These transaction counts describe deal activity, not the expected returns or defensibility of publicly traded software stocks.
In a survey of more than 200 private-equity investors, strategic acquirers, and SaaS CEOs, Software Equity Group reported that 85% of buyers identified AI-driven commoditization as the largest risk to SaaS value. That is a report of surveyed buyers’ views, not an 85% measured chance that software businesses will be displaced.
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PwC Strategy& partner Erik Wall, writing on March 16, 2026, said the broad sell-off in Bessemer Venture Partners index companies raised the question of whether public markets were distinguishing defensible business models from those at higher risk of redundancy in the AI era. The implication for an individual investor is to investigate differentiation company by company rather than infer that a sector-wide decline has created a uniform opportunity.
Track indicators that can change the thesis
After each earnings report, compare new evidence with the assumptions in your valuation and business analysis. Useful indicators include:
- Gross and net retention, customer additions, expansions, and churn commentary.
- Pricing, seat counts, contract structure, and signs of competitive repricing.
- Product usage and AI feature adoption, alongside any disclosed paid usage or AI revenue.
- Customer references and evidence that adoption affects renewals or measurable customer outcomes.
- Gross-margin movement, inference costs, and reported support or engineering efficiency.
- Cash flow, dilution, and whether growth depends on greater reinvestment.
These are diligence indicators to monitor, not a claim that every issuer reports them. For a named stock, confirm the details in current company filings and earnings releases, and use a stated valuation date and explicit assumptions. Professional market studies and buyer surveys provide context, but they do not replace issuer disclosures or establish the value of a particular security.
Use the same comparison framework for every company
When comparing two software stocks, assess each on the same eight dimensions rather than letting a compelling AI narrative dominate the decision:
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- What proprietary data, data rights, or distribution does the company control?
- How much of the paid task is exposed to agent substitution?
- How much domain-specific or regulated complexity does the product handle?
- What do retention, customer concentration, and pricing behavior say about customer dependence?
- Is AI being adopted and monetized, and is there evidence of improved customer outcomes?
- What are gross margin, cash flow, dilution, and reinvestment requirements?
- How does the valuation compare under similar growth expectations and downside assumptions?
No single dimension answers the investment question. A business can be defensible but overvalued, or attractively priced because its underlying economics are deteriorating. The decision turns on the combination of evidence about substitution, customer value, financial quality, and price.
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