How much is your company’s data worth to an AI lab? Most business owners have no idea that their operational data has a real dollar value, and that changed dramatically in 2026 when Spirit Airlines’ bankruptcy records attracted a reported $12.5 million bid from a consortium of AI training data buyers. That deal put the question on every entrepreneur’s radar: if a bankrupt airline’s data is worth eight figures, what is a healthy, active business’s data worth?

The answer depends on several variables, and the ranges are wider than most people expect. This guide breaks down real dollar ranges by industry, explains what drives value up or down, and shows you how to get an actual valuation for your company’s data today.

How Data Licensing Deals Are Priced

AI labs are not buying raw files. They are buying structured, real-world decision data: the kind of information that shows how humans solve problems, make judgments, communicate, and navigate complex workflows. Your CRM history, support tickets, financial records, and internal communications are all potential training material for the next generation of AI models.

Pricing in the AI data licensing market is driven by four core variables:

1. Industry Vertical

Industries with complex, high-stakes decision-making produce the most valuable training data. Legal, healthcare, and financial services top the list because AI models trained on these datasets can command premium pricing from enterprise customers. Retail and hospitality data tends to be lower value because much of it is already publicly scraped or widely available.

2. Headcount

More employees typically means more data volume, more workflow diversity, and more decision trails. A 200-person law firm has far more decision-dense data than a 5-person shop, and buyers price that accordingly. Headcount tiers are one of the first qualification questions every platform asks.

3. Years of History

Data that goes back five or more years is significantly more valuable than data from a company that launched two years ago. Long-horizon datasets capture how decisions evolved, how workflows matured, and how businesses adapted to changing conditions. That temporal depth is hard to replicate artificially.

4. Software Stack Depth

Companies running deep software stacks, such as Salesforce, Zendesk, QuickBooks, or Stripe alongside internal tools, produce structured, machine-readable data that is far easier for AI buyers to process. Companies operating on spreadsheets and email alone produce data that requires heavy cleaning and normalization, which reduces its value.

Real Dollar Ranges by Industry

These ranges reflect deal structures observed in the AI data licensing market in 2025 and 2026. They represent one-time or multi-year licensing fees, not ongoing royalties, and vary significantly based on the four variables above.

Legal Firms ($200K – $800K)

Law firms produce some of the most valuable operational data in the market. Contract review threads, case strategy notes, deposition prep workflows, and client communication patterns are exactly what legal AI companies like Harvey and Lexis+ AI need to train their models. Mid-size firms with 20 or more attorneys and five or more years of case history are consistently landing deals in the $400K to $800K range.

Healthcare Practices ($300K – $1M+)

Healthcare data commands premium pricing because of its complexity and scarcity. Clinical decision workflows, triage logs, billing dispute resolution, and patient communication patterns are valuable training inputs for medical AI. Practices with electronic health record integration and five-plus years of patient history are the most sought-after. HIPAA compliance adds friction but does not disqualify most deals; buyers handle anonymization as part of the process.

Financial Services ($200K – $900K)

RIAs, mortgage brokers, accounting firms, and credit unions all produce high-value decision data. Underwriting rationale, investment recommendation trails, tax planning workflows, and compliance documentation are all in demand. Firms with QuickBooks or Stripe deeply integrated into their operations tend to score higher on the software depth variable.

Insurance ($150K – $500K)

Independent insurance agencies and MGAs have underappreciated data assets. Claims handling decisions, risk scoring rationale, and policy renewal communications are all valuable. The key differentiator for insurance data is the depth of claims history: firms with seven or more years of claims data consistently outperform on deal value.

Logistics and 3PLs ($150K – $600K)

Third-party logistics companies produce rich operational data: carrier selection decisions, exception handling logs, routing optimization trials, and warehouse workflow patterns. Companies with proprietary TMS platforms or deep integrations score higher. Asset-based carriers with their own equipment tend to command more than pure broker operations.

SaaS and Tech Companies ($100K – $400K)

B2B SaaS companies produce valuable support and onboarding data, but pure-tech firms are generally lower on the priority list because much of their interaction data is text-based and relatively easy to synthesize. The exception is companies with complex enterprise customer support workflows running on Zendesk or similar platforms with years of ticket history.

Professional Services ($75K – $300K)

Management consultants, marketing agencies, HR firms, and staffing companies all have potentially licensable data, but the value depends heavily on whether workflows are documented in software rather than email. Firms with project management platforms, CRM systems, and structured client reporting tend to qualify for the higher end of this range.

Retail and Hospitality ($50K – $200K)

Retail and hospitality data is the most commoditized category. Much consumer-facing data is already available from other sources, which depresses pricing. The exception is companies with deep inventory management systems, sophisticated demand forecasting workflows, or unusual operational complexity such as multi-location restaurant groups or specialty retailers.

How Company Size Affects Your Deal Value

Headcount is one of the strongest predictors of deal size because it correlates with data volume, workflow diversity, and organizational complexity. Here are the typical ranges by headcount tier:

• 5-15 employees: $50K – $150K (Mode.inc is typically the best fit at this size)
• 15-50 employees: $100K – $400K (most deals in this range go through Polyshares or Mode)
• 50-150 employees: $250K – $700K (Polyshares handles most deals at this tier)
• 150-500 employees: $400K – $1.5M (direct negotiations often involve multiple platforms)
• 500+ employees: $1M+ (enterprise-level deals, often multi-year licensing agreements)

These ranges assume average software depth and industry. High-value industries like healthcare and legal can push any headcount tier significantly higher. See our guide on why your business data is now worth real money in the AI gold rush for more context on how this market developed.

What Increases Your Data’s Value

Several factors can push your deal significantly above the midpoint of your industry range:

• Organized, structured records: Data that is already clean, labeled, and stored in modern software is dramatically easier for buyers to process. Every hour they don’t spend cleaning your data is value that comes back to you in the offer price.
• Rare vertical or niche specialization: A workers’ comp insurance MGA operating in a specific state niche has data that is far harder to replicate than a generalist insurance agency. Scarcity drives value.
• Long history (7+ years): Multi-year datasets that show how decisions evolved over time are premium inputs for model training. The longer your clean history, the better.
• Deep software integration: Companies running Salesforce alongside specialized vertical software produce richer, more interconnected data than companies running a single tool.
• Human decision documentation: Companies where employees document their reasoning, not just their actions, produce the most valuable training data. Annotated workflows, decision trees, and escalation logs are highly sought after.

What Decreases Your Data’s Value

Equally important is understanding what can push your deal below the midpoint or disqualify it entirely:

• Mostly consumer-facing data: B2C transaction data is generally lower value than B2B operational data. Consumer data is commoditized; business decision data is not.
• Shallow software use: If your team primarily uses email, spreadsheets, and phone calls with no structured software layer, your data is difficult to extract and low in machine-readable value.
• Under 2 years of history: New businesses often do not qualify at all. Most platforms require at least 18-24 months of operational history before they will evaluate a dataset.
• Disorganized records: Inconsistent naming conventions, data spread across multiple incompatible systems, and large gaps in the historical record all reduce what buyers will pay.
• High consumer PII content without clear anonymization path: If your data is deeply intertwined with consumer personal information and there is no clean way to anonymize it, buyers will either pass or reduce their offer significantly.

How to Get an Actual Valuation

Reading industry ranges is useful, but getting an actual number for your specific company requires submitting to a platform that can assess your data directly. Here is the step-by-step process:

Step 1: Submit to Polyshares

Polyshares is the platform we recommend for most businesses. It operates as a principal buyer, meaning it purchases your data directly rather than simply brokering it. There is no headcount minimum, deals range from $100K to $2M+, and the process starts with a qualification intake that does not require you to share any actual data. Submit your intake at Polyshares here.

Step 2: Get a Mode.inc Assessment

Mode.inc specializes in legal and professional services firms and accepts companies from as few as 6 employees. If your company is in those verticals or on the smaller end of the headcount scale, Mode is worth a parallel assessment. Submit your company at Mode.inc here.

Step 3: Compare Offers

Because most deals are non-exclusive, you are not obligated to choose one platform. Getting assessments from multiple buyers gives you leverage and a real market view of what your data is worth. Do not accept the first number without checking alternatives. Read our comparison of the top companies buying business data for AI training to understand the full landscape before you decide.

The AI data licensing market is real, active, and moving fast. Business owners who understand the value of what they have built are capturing significant one-time payouts that most of their competitors are leaving on the table. The first step is knowing your range. The second step is getting an actual offer.

For more resources on building and monetizing your business assets, join Hustler’s Library free and get access to our full library of operator guides.

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