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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteEvaluate an AI-exposed software stock by asking whether customers still need the job the product does, whether competitors can reproduce that value, and whether the vendor can retain customers and earn acceptable returns as AI changes pricing and costs. Then assess the company’s financial quality and the stock’s valuation separately: a strong business can be overpriced, and a low multiple alone does not prove a stock is a bargain.
This is a framework for comparing software businesses, not a recommendation on a particular stock. AI does not create the same risk for every software company. A product that is mainly a replaceable interface or feature faces a different threat from a platform embedded in essential workflows, supported by valuable data rights, or built around deep industry expertise. Neither category is automatically safe or doomed.
Start with the customer’s job, not the AI feature list
Identify what customers pay to accomplish
For each product, write down the user, buyer, task, frequency of use, measurable result, and current alternative. Separate the underlying job—such as maintaining a system of record or processing a transaction—from the screen, feature, or assistant through which customers access it. The more a product’s paid value depends on a function that AI can reproduce cheaply, the more exposed it may be to substitution or price competition.
Ask whether an AI agent can replace the whole process
Do not stop at whether a chatbot can imitate one feature. Consider whether an AI agent could complete the customer’s task end to end, including obtaining permissioned data, making changes in connected systems, and producing an auditable result. The consequences of an error matter: tasks involving material operational, financial, or regulatory risk may require controls and accountability that a standalone AI tool does not provide.
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That is a way to frame the risk, not proof that a product will or will not be displaced. A vendor’s AI announcement is not evidence by itself that customers are adopting the feature, renewing because of it, or paying more for it.
Test whether the moat is real and relevant to AI
PwC Strategy& describes essential workflows, unique data, and deep industry expertise as sources of advantage that AI may strengthen. Its analysis also flags surface-level features and seat-based growth models as more exposed to lower barriers and faster competition. These are useful diligence prompts, not guarantees of future performance.
Strategy& also identifies several characteristics to investigate. For each one, look for evidence that customers rely on it and that a rival or AI entrant cannot readily reproduce it:
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- Workflow embeddedness: Is the software used in a daily, mission-critical process, or can customers replace it without disrupting their work?
- Data and rights: Does the vendor have access to data that is both valuable and lawfully usable for the product, or is similar information available to competitors?
- Vertical expertise: Does the product encode specialized industry knowledge, or is its value mostly general-purpose functionality?
- Regulation and compliance: Do the customer’s requirements make auditability, permissions, or accountable operation important to the purchase?
- Services or hardware: Are implementation services or connected hardware integral to the solution, rather than incidental add-ons?
Integration depth and switching friction can support these advantages, but check that they show up in actual customer dependence. A long list of integrations or a claim of proprietary data is not enough if customers can switch easily or competitors can obtain comparable inputs and distribution.
Separate defending the business from monetizing AI
A software company can use AI to protect its place in a workflow without charging customers more for it. It might also expand the work it can serve while accepting lower revenue per seat. PwC frames this as a shift from selling access to a tool toward delivering outcomes; whether a particular vendor benefits depends on what customers actually adopt and pay for.
Check company disclosures and customer evidence for paid usage, product adoption, renewal behavior, measurable customer return, and changes in contract structure. Distinguish recurring revenue from an AI product from trials, bundled access, or management’s forecast of a future opportunity. If the vendor charges by consumption or outcome rather than by seat, investigate what drives the bill and whether customers continue to see value at that price.
AI can also add costs. Examine inference expenses and whether AI reduces the company’s own support or delivery costs. A feature that customers like may still weaken economics if it increases compute or service costs without improving retention, revenue, or efficiency enough to compensate.
Check the revenue engine and operating quality
Traditional software metrics remain relevant because AI risk ultimately has to show up in customer behavior and financial results. Software Equity Group’s 2026 summary of buyer priorities says buyers consider ARR scale and growth, gross and net retention, profitability, and Rule of 40 among first-gate measures. It also reports scrutiny of gross margin, customer acquisition cost (CAC) payback, and annual contract value (ACV).
- Retention: Gross retention helps reveal revenue lost from existing customers; net retention also reflects expansion or contraction within that customer base. Read both alongside customer adds and concentration.
- Growth and sales efficiency: Consider recurring-revenue growth with CAC payback and ACV. Growth that requires increasingly expensive selling deserves a different assessment from growth supported by efficient customer acquisition and expansion.
- Margins and cash generation: Track gross margin and cash flow, including whether AI-related costs are changing either. Profitability should be read together with reinvestment needs.
- Rule of 40: This heuristic combines growth and profit margin. It is not a substitute for examining cash conversion, accounting quality, or how the company funds growth.
- Dilution: Include stock-based compensation and resulting share dilution when considering what the business earns for shareholders.
These measures can help identify deterioration or resilience; none alone establishes that AI caused a change. Compare trends over time and investigate management explanations against customer behavior and reported economics.
Use market multiples as context, not as a buy signal
Published software multiples vary by index, period, and business category. The figures below are historical snapshots reported by PwC Strategy& and Software Equity Group (SEG), not current quotes, intrinsic-value estimates, or directly comparable fair-value conclusions.
| Measure | Reported figure | Source and qualification |
|---|---|---|
| EV / one-year-forward sales | 9.0x to 5.6x, a 40% decline | PwC Strategy& reported that the median multiple for Bessemer Venture Partners index Rule-of-40 companies fell over the prior 12 months in its March 16, 2026 analysis. It described the move as a broad reset in risk premiums. |
| EV / trailing-12-month revenue | 4.8x median | SEG SaaS Index at 4Q25, as reported in 2026. |
| EV / trailing-12-month revenue by SEG category | ERP & Supply Chain: 6.7x; Security: 6.3x; Financial Applications: 5.3x; Vertically Focused: 4.6x; Analytics & Data Management: 4.5x | SEG SaaS Index category medians at 4Q25, as reported in 2026. |
| EBITDA margin | 9.1% median | SEG SaaS Index for 2025, as reported in 2026. |
The difference between category medians is one reason not to apply a single “software multiple” to every company. Peer comparisons are more informative when businesses also resemble one another in growth, profitability, capital intensity, customer mix, and risk. Even then, a comparable-company multiple is a reference point—not a conclusion about what a stock is worth.
To value a specific company, state the assumptions rather than relying on a sector average. A scenario-based discounted cash flow or comparable-company analysis can make assumptions about growth, margins, reinvestment, dilution, and discount rate explicit. Build downside, base, and upside cases that account for customer loss, seat compression, AI compute expense, competitive repricing, successful AI monetization, and operating leverage. A falling multiple might reflect a temporary risk premium or lasting business deterioration; the multiple alone cannot distinguish them.
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Use sector evidence carefully
SEG reported 2,698 SaaS mergers and acquisitions in 2025, approximately 58% of total software M&A activity, and said approximately 72% of SaaS deals were “AI-referenced.” SEG’s definition includes deal materials mentioning AI capabilities, integrations, or data-infrastructure relevance. It does not mean that this share of deals involved pure-play AI companies, or establish that AI drove the deal value.
In a survey of more than 200 private-equity investors, strategic acquirers, and SaaS CEOs, SEG reported that 85% of buyers identified AI-driven commoditization as the largest risk to SaaS value. That is a survey of buyer views, not a measured probability that software businesses will be displaced. Treat it as evidence of concern in the market, not a forecast for an individual company.
Compare companies on the same evidence
When comparing two or more software stocks, use the same questions for each company instead of letting an appealing AI story dominate the analysis:
- How critical is the customer workflow, and what would switching cost in time, money, or disruption?
- What proprietary data rights, distribution, or integration advantages does the company control?
- How much of the paid task could an AI agent replace, and what constraints stand in the way?
- Does regulated or domain-specific complexity add value customers demonstrably depend on?
- What do retention, customer concentration, and pricing behavior indicate about customer dependence?
- Is AI adoption paid and affecting customer outcomes or renewals, rather than limited to trials or bundled access?
- How are gross margin, cash flow, dilution, and reinvestment needs changing?
- Does the valuation still make sense under comparable growth assumptions and a credible downside case?
No single answer replaces the others. A durable product can still be a poor investment at an excessive price, while a low-priced stock can reflect a business facing persistent erosion.
Update the thesis as results arrive
After each earnings report, check for changes in retention, customer additions and expansions, pricing and contract structure, product usage, AI feature adoption or revenue, gross margin, support or engineering efficiency, and customer churn commentary. Seek customer references where possible. These are indicators to monitor; they are not measures every issuer necessarily discloses.
For analysis of a named security, use that company’s current filings, earnings releases, and market price, and state the valuation date and assumptions. The sector-level market studies cited here are useful context, but they are not substitutes for issuer disclosures or a company-specific valuation.
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