The integration of AI into software products is forcing vendors and customers to rethink traditional data usage agreements. Unlike legacy software confined to operating solely within an individual customer’s data, AI models frequently learn from data pooled across multiple clients, raising questions about consent, competitive advantage, and the terms under which customer data is shared.
- AI features often learn from pooled customer data, affecting multiple clients.
- Contracts rarely clarify data sharing terms or customer benefits for contribution.
- Customer awareness and opt-in consent for sharing are increasingly demanded.
What happened
As AI gains prominence in software products, vendors are leveraging pooled data from multiple customers to train models that improve features like contact enrichment and lead scoring. This approach contrasts with traditional software usage, which operated only on a single customer's data without cross-sharing. One prominent case involved HubSpot, which announced plans to aggregate enrichment data from customer accounts by default to enhance prospecting tools. The move sparked immediate backlash, particularly from sales and revenue operations teams concerned about the unintended competitive advantage being given to others.
In response, HubSpot quickly reversed its policy, committing that future enrichment features relying on customer data would be opt-in rather than mandatory. This incident spotlighted a widespread industry challenge: many vendors’ contracts do not explicitly address whether customer data will be used to benefit others and under what terms, leaving customers uncertain about data control and reciprocal value.
Why it matters
The shift to AI-enabled platforms effectively transforms software vendors into data companies, where the value of the service depends heavily on the aggregation and sharing of customer data. However, this transformation has outpaced the development of clear commercial and governance frameworks. Without explicit agreements, customers may unintentionally subsidize competitors by contributing valuable data and insights without adequate compensation or benefit.
Industry research shows that only a minority of organizations currently have mature AI strategies and governance in place, which contributes to confusion around data rights and responsibilities. Customers are increasingly scrutinizing how their data is used, demanding transparency and control. The eventual establishment of standard contract language addressing AI data usage—similar to how security certifications and service guarantees became norms—is anticipated as procurement functions push for safeguards and fair terms.
What to watch next
Vendors and buyers alike will need to refine contract terms to explicitly state if AI training and enrichment use customer data from across accounts, whether participation is voluntary, and what unique benefits contributors receive. Buyers will insist that opting out of data pooling does not cause loss of core functionalities, posing technical and commercial challenges for vendors.
Another looming issue is exit rights: once a customer’s data has trained a model, removing their influence is highly complex and costly due to the nature of machine learning. This gap highlights the need for innovative solutions or regulatory guidance to protect customer data ownership and competitive interests in the evolving AI software marketplace.