How GenAI Features Affect The Buying Cycle And What Go-To-Market Leaders Can Do About It

GenAI evaluation and architecture review. Incorporate genAI-specific evaluation into the EA review process. This includes assessing solution fit, model quality, integration architecture, data governance, and explainability requirements, as well as validating that genAI components align with enterprise strategy and technology standards.

Product management decision-makers should treat genAI as a core product management capability , not an experiment. This is a snapshot of Forrester’s data on the use of genAI in product management.

The embedding of generative AI (genAI) features into products has moved rapidly from experimentation to near ubiquity. In 2025, 84% of product managers said that their firm embeds genAI into at least some of its products, up from 58% in 2024, signaling that AI-enabled functionality is now standard rather than exceptional. While only 12% said that genAI is embedded into all products, very few companies remain on the sidelines with no genAI features at all.

This is a snapshot of Forrester’s data on global online adults’ expectations for genAI in consumer interactions.

Usage And Impact Grows As Buyers Use Public And Private GenAI Tools

How GenAI Is Changing The Product Lifecycle

GenAI Makes Knowledge Management Cool Again

Nearly half of portfolio marketing decision-makers report their organization is not adopting genAI for portfolio marketing or product management, including 8% who have no interest in using it at all. Only 11% are expanding its use beyond their current implementation. Broadly speaking, portfolio marketers are using or plan to use genAI to conduct buyer research, develop content, and create messaging.

It’s a goldmine, and companies are finding it much easier to work with by having LLMs and genAI tools available. According to Forrester’s Data And Analytics Survey, 2025, data and analytics decision-makers report, on average, that 36% of the data their organization manages is structured, 30% is semistructured, and 34% is unstructured. However, unstructured data is still often siloed in disparate systems, without proper tags, and not readily usable for genAI.