Selling AI in B2B SaaS : How AI-native and incumbent B2B SaaS firms communicate value and trust in enterprise AI markets
Khan, Ali Ayub (2026)
2026
Master's Programme in Business and Technology
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
Hyväksymispäivämäärä
2026-05-20
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202605195923
https://urn.fi/URN:NBN:fi:tuni-202605195923
Tiivistelmä
Enterprise artificial intelligence (AI) creates a practical communication problem for business-to-business software-as-a-service (B2B SaaS) vendors. Enterprise buyers are asked to evaluate systems whose quality, governance, security and business impact are difficult to judge before implementation, while vendors must make their AI offerings understandable and credible through public communication. This thesis examines how AI-native and incumbent B2B SaaS firms convey the value and credibility of AI solutions to enterprise buyers.
The study is performed as a qualitative document-based comparative study. The data consist of public documents and webpages produced by B2B SaaS vendors, including AI landing pages, product pages, pricing pages, trust and security pages, and selected case studies. The sample includes 40 companies: 20 AI-native firms and 20 incumbent firms. Qualitative content analysis is used to code value proposition communication, message orientation, trust signals, monetization models, and alignment between value claims and commercial logic.
Both cohorts primarily use outcome-focused value claims rather than purely technical feature claims. However, the cohorts differ in how they establish credibility. AI-native firms more often use formal assurance, safety language, certifications, customer logos, and external validation to compensate for weaker historical legitimacy. Incumbent firms more often rely on installed-base legitimacy, enterprise governance, brand continuity, and scale. The study also finds that outcome-focused messaging is more developed than outcome-linked monetization: many vendors communicate AI as a source of business impact while still pricing it through familiar SaaS access, seat, usage, or enterprise-license models.
The thesis contributes by connecting value-based selling, customer value proposition literature, and signaling theory in the context of public enterprise AI communication. It shows that AI-native and incumbent firms are converging in the language of value but diverging in the construction of trust.
The study is performed as a qualitative document-based comparative study. The data consist of public documents and webpages produced by B2B SaaS vendors, including AI landing pages, product pages, pricing pages, trust and security pages, and selected case studies. The sample includes 40 companies: 20 AI-native firms and 20 incumbent firms. Qualitative content analysis is used to code value proposition communication, message orientation, trust signals, monetization models, and alignment between value claims and commercial logic.
Both cohorts primarily use outcome-focused value claims rather than purely technical feature claims. However, the cohorts differ in how they establish credibility. AI-native firms more often use formal assurance, safety language, certifications, customer logos, and external validation to compensate for weaker historical legitimacy. Incumbent firms more often rely on installed-base legitimacy, enterprise governance, brand continuity, and scale. The study also finds that outcome-focused messaging is more developed than outcome-linked monetization: many vendors communicate AI as a source of business impact while still pricing it through familiar SaaS access, seat, usage, or enterprise-license models.
The thesis contributes by connecting value-based selling, customer value proposition literature, and signaling theory in the context of public enterprise AI communication. It shows that AI-native and incumbent firms are converging in the language of value but diverging in the construction of trust.