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Long before anyone trained a model on customer data, the marketing data business worked out the terms for pooling it. Catalog dealers in the 1990s joined data cooperatives that combined their transaction histories so each member could find new buyers, and the arrangements held because the rules were blunt.
A retailer contributed data to obtain modeled prospects; no member received a competitor’s customer file; and a company that stopped contributing stopped receiving. Card fraud consortia operate on similar give-and-take terms. No one would call these systems elegant, but every participant understood the trade.
Software vendors that build AI features now run into the same question, usually without the rules. Traditional software sat on top of a customer’s data and operated on it. AI products learn from data, and on a multi-tenant platform, that learning rarely stays in a single account.
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One company’s records can sharpen enrichment, lead scoring and recommendations delivered to thousands of others. A seller can improve his product for each customer by relying on the work of a few, and most contracts say nothing about whether this trade is fair. The customer still owns their data in all legal senses.
Ownership alone does not answer who benefits.
Users are pushing back to share hard-earned data
HubSpot learned how touchy the issue became when it announced plans to pool enrichment data across customer accounts, including business contact details, employer information, and email delivery signals, to improve prospect records platform-wide.
Every account was by default. Within days, the company dropped the plan and promised that any future enrichment feature built on customer data would be opt-in. HubSpot had clearly stated that core CRM records would never be shared without permission, so privacy in the usual sense was not what upset the sales and RevOps teams, who pushed back. Her concern was more difficult to answer. Years of work cleaning and enriching their own contact data were in the process of improving the prospecting tools available to every other client, including competitors, and no one had asked what they were getting for it. The product terms that allowed the change were updated almost two years before most customers heard about them.
AI creates random data companies
Many software companies adding AI tools have never had to think like this. They are dating companies one model at a time, often without deciding. Whether a customer’s information feeds a shared model is often determined by a product team working on a launch date rather than by anyone who owns the commercial relationship, and the answer is couched in terms few buyers will read.
McKinsey’s 2026 survey on AI confidence found that only about 30% of organizations have reached a mature level in AI strategy, governance and controls. Pooled training data tends to fall between legal, product and sales, without a single team feeling responsible for explaining it.
The need for strong exit clauses
Buyers will force the issue long before regulators do. Security certifications and uptime guarantees became standard contract language over the last decade because enough customers asked for them that providers stopped arguing, and AI data terms are down the same road. Procurement teams are beginning to question whether their data is training or enriching models used by other customers, whether participation is opt-in, and whether it can be turned off later without losing core features. The more demanding buyers want to know what a contributing customer gets, what a non-contributing customer doesn’t.
Exit conditions will be the hardest part to write. A customer can cancel a contract and reclaim its records, but data that has already formed a production model cannot be pulled out cleanly. Retraining is expensive, and removing a customer’s influence from a live model remains an unsolved problem for most vendors. A vendor can honestly say it has erased every record a customer has ever uploaded and still runs a model those records helped build. Contracts written for the storage are assumed that the deletion of data has ended its use. Contracts written for AI must clearly state what will remain after a customer leaves, and whether the customer agreed to it when they signed.
Opt-in consent answers part of the question. A checkbox tells a seller that it can use the data, but it doesn’t say anything about the return, and a customer who opted for nothing in particular got a bad deal, even if it’s legal. The old cooperatives signed up members because the members see exactly what they are getting.
Providers who explain their data economy before customers ask have an easier time with each renewal. The statement belongs in the product and in the contract, and it should cover what customer data feed shared models, what these models produce and what the contributors get back. Security questionnaires were once a formality, until buyers made them a condition of the sale. Pooled data requirements are on the same schedule, and providers who write clearly now set the standard that the rest of the market follows.