--- title: "How to Value an AI Company in 2026: The 6-Number Worksheet Investors Actually Run" slug: how-to-value-ai-company-worksheet date: 2026-05-06 url: https://beyondelevation.com/blog/post.html?slug=how-to-value-ai-company-worksheet author: Hayat Amin site: Beyond Elevation --- # How to Value an AI Company in 2026: The 6-Number Worksheet Investors Actually Run Two AI companies. Same revenue. Same team size. Same market. One valued at $30M. The other at $300M. Hayat Amin sees this pricing gap every quarter — and the answer is never the pitch deck, the demo, or the founder's charisma. It is six numbers on a worksheet that most founders have never seen. Knowing how to value an AI company in 2026 is no longer optional. Median AI startup multiples sit at 20–30x revenue, but dispersion stretches from 10x to 50x. The difference between a fair valuation and a premium one comes down to six factors investors actually measure. Beyond Elevation has run this diagnostic on portfolio companies for two years. Hayat Amin's 6-Number AI Valuation Worksheet distils the investor scoring model into a framework any founder can use — and more importantly, can engineer upward before their next round. ## What Are the 6 Numbers Investors Use to Value an AI Company? The 6-number AI company valuation worksheet measures recurring revenue quality, IP asset strength, data moat depth, switching cost, addressable market ceiling, and defensibility composite. Each number carries a weight. Each can be improved. Most founders optimise only for the first one and leave the rest to chance — which is why two companies with identical revenue price 10x apart. ## Number 1: Recurring Revenue Quality Revenue quality is the baseline investors use to set the floor. They score it on net revenue retention (NRR), contract length, and customer concentration. An AI company with 130% NRR and no single customer exceeding 8% of revenue scores top-quartile. One with 90% NRR and a single customer at 40% gets a discount that compounds across every other line. The 2026 benchmark: median AI SaaS NRR sits at 115%. Companies above 125% consistently price in the top third of the valuation band. Below 100%, and the conversation shifts from growth equity to turnaround — regardless of what the model can do. ## Number 2: How to Value an AI Company's IP Assets IP asset strength is the number most founders undervalue — and the one with the widest scoring gap. Investors assess patent portfolio depth, trade secret documentation, and the defensibility window: how long it would take a well-funded competitor to replicate your core innovations. The data is clear: AI companies with structured patent portfolios command a 15–20% valuation premium over comparable unprotected companies. An independent IP audit adds another 15–20%. On a $50M baseline, the IP premium alone is worth $15–20M in enterprise value. Hayat Amin argues this is the most mispriced line on the worksheet. "Founders spend months optimising NRR by two points," Hayat Amin says, "then leave seven figures of IP premium on the table because nobody told them to file before the term sheet." Companies with patents are 10.2x more likely to secure early-stage funding. That stat changes term sheets. Beyond Elevation runs an [IP capture audit](/blog/posts/ai-engineering-ip-what-is-protectable/) on every AI portfolio company to move this score before the round opens. ## Number 3: Data Moat Depth The data moat measures how unique, proprietary, and difficult to replicate your training and operational data is. VCs in 2026 explicitly score defensibility on proprietary data flywheels — the mechanism by which your product generates data that improves the product, which generates more data, creating a compounding advantage competitors cannot shortcut. The scoring is binary at the extremes. Public data only scores zero. Proprietary data with exclusive licensing agreements and documented provenance scores maximum. Most AI companies fall in the middle — some unique data, poorly documented, with ownership rights that would not survive due diligence. The data monetisation market is growing from $4.05B to $16.11B by 2034. Top performers earn 11% of revenue from data versus 2% for peers — a 5x gap. The companies capturing this premium are not the ones with the most data. They are the ones who [structured their data as a licensable asset](/blog/posts/data-monetization-strategy-framework/) early. ## Number 4: Switching Cost and Retention Switching cost quantifies how deeply your AI product is embedded in the customer's workflow. A standalone AI widget scores low. An AI system woven into core business processes — where removal means retraining teams, rebuilding integrations, and losing accumulated performance history — scores high. Investors measure implementation time (longer is better for lock-in), workflow integration points (more is better), and custom model fine-tuning on customer data (irreplaceable once trained). The GPT-wrapper rejection is now table stakes. If your product can be replaced by a competitor in a weekend, the switching cost score is zero — and the valuation reflects it. Enterprise AI companies with implementation times exceeding 90 days and five or more integration points achieve 30–40% higher retention rates. Retention is the number VCs extrapolate into terminal value. ## Number 5: Addressable Market Ceiling Market ceiling is the maximum revenue your AI company could realistically capture. Investors discount inflated TAM figures aggressively. The number that matters is serviceable obtainable market (SOM) — what you can capture in 3–5 years given current product, team, and go-to-market motion. AI companies consistently overstate TAM by 5–10x. Hayat Amin reminds founders that a $100B TAM slide means nothing if the SOM is $50M. Investors model from the bottom up: current customers multiplied by expansion path multiplied by new segment penetration. A credible $200M SOM in a $2B SAM outscores a hand-wavy $50B TAM every time. ## Number 6: The Defensibility Composite — Where AI Company Valuations Are Won The defensibility composite is the meta-score that rolls Numbers 2 through 5 into a single measure of how hard it is to kill this company. Investors score it across five axes: proprietary data flywheels, workflow integration depth, persistent memory and knowledge systems, switching cost, and vertical specialisation. This is where the worksheet becomes exponential. A high score on IP (Number 2) multiplies the value of a high data moat (Number 3). Patent-protected proprietary data is worth dramatically more than unprotected proprietary data — because the protection makes the moat permanent rather than temporary. Global AI spend hit $2.52T in 2026, and the valuation premium is concentrating in commercial execution and data quality — not algorithmic novelty. The [2026 AI multiples data](/blog/posts/ai-business-worth-2026-multiples/) confirms it: the top quartile commands 40x+ while the bottom quartile scrapes 10x. The gap is defensibility, not revenue. ## How to Score Higher on Every Number The 6-number worksheet is not a passive report card. It is an engineering target. Every number can be moved — and the highest-ROI moves almost always sit on the IP and data lines. File provisional patents on core innovations before the next round opens. Document trade secrets with formal classification and access controls. Structure data rights so proprietary datasets are unambiguously owned and licensable. Build switching costs by deepening workflow integration and customer-specific fine-tuning. These are not 12-month projects. Beyond Elevation has moved IP scores from zero to fundable in 90 days for pre-Series A companies — because the innovations already existed, they just had not been captured. The founder who understands how to value an AI company is the founder who engineers the inputs before the investor runs the model. The worksheet is not secret. The numbers are not hidden. The advantage goes to founders who optimise for all six — not just revenue. Book a strategy session at [beyondelevation.com](https://beyondelevation.com) to run the 6-number diagnostic on your company before your next raise. --- ### You just read the framework. Now price your own IP. Beyond Elevation runs a 60-minute IP & licensing diagnostic for founders raising Seed–Series B. You leave with: (1) a defensibility score, (2) the royalty range your current portfolio supports, (3) the next 3 filings ranked by exit-multiple impact. No deck. No proposal. One call, one number. [Book the diagnostic →](https://usemotion.com/meet/hayat-amin/be?ref=blog-how-to-value-ai-company-worksheet) *14 founders booked this month. Hayat takes 4/week.* --- ## FAQ ### How do you value an AI company in 2026? You value an AI company in 2026 by scoring it across six factors: recurring revenue quality, IP asset strength, data moat depth, switching cost, addressable market ceiling, and defensibility composite. Median AI startup multiples range from 20–30x revenue, with dispersion from 10x to 50x driven primarily by IP and data defensibility scores. ### What multiple do AI companies trade at in 2026? Public AI companies trade at 25–35x revenue. Private AI startups range from 15–30x with a late-stage median of 25.8x. IP-rich AI companies command a 15–20% premium above these baselines, and companies with independent IP audits achieve an additional 15–20% uplift. ### Why do two similar AI companies have such different valuations? Valuation dispersion in AI is driven by defensibility — specifically IP protection, data moat quality, and switching costs. Two companies with identical revenue but different defensibility profiles price dramatically apart because investors buy the durability of future cash flows, not just current performance. ### How does intellectual property affect AI company valuation? AI companies with structured patent portfolios receive 15–20% higher valuations than comparable unprotected companies. Patents and documented trade secrets reduce investor risk by extending the defensibility window and creating barriers competitors cannot engineer around, translating directly into higher multiples. ### What is the fastest way to increase an AI company's valuation? The fastest lever for most AI companies is IP capture — filing provisional patents on existing innovations, documenting trade secrets, and structuring data rights. These moves take 60–90 days and improve the two worksheet numbers (IP asset strength and defensibility composite) that drive the widest valuation gaps. --- *Published on [Beyond Elevation](https://beyondelevation.com) — IP Strategy & Licensing Revenue Consultancy*