96% of B2B Marketers Use AI — But Only 44% Have the Data Infrastructure
Adobe and Demand Gen Report's 2026 survey reveals the clearest articulation yet of where enterprise AI stalls: not in the tools, but in the plumbing. The 52-point gap between AI adoption and data readiness is the number every IT leader evaluating AI investments should carry into the next budget conversation.
Read on MarketScale →J.P. Morgan: SaaS Monetization Shifts from Seats to Usage and Outcomes
Traditional seat-based SaaS pricing is giving way to consumption- and outcome-based models. AI is transforming how software vendors capture value, creating a clear separation between AI-native and non-AI software companies. Enterprises increasingly purchase AI solutions rather than building them internally.
Read on J.P. Morgan →ChatGPT Advertising Enters Beta with Full Campaign Management
OpenAI's Ads Manager Beta now supports campaign management and performance reporting — evolving quickly from an experiment into something that resembles a performance-media platform. As AI agents handle more discovery, this is where buyer attention is moving.
Read on Saurce →Gemini Closes Gap on ChatGPT: 27% vs 53% Market Share
Similarweb data shows ChatGPT's share of global generative AI web traffic falling from 76% to 53%, while Gemini climbed to ~28%. Google reports 950 million monthly active Gemini users. The reason? Distribution. Gemini connects across Search, Workspace, Gmail, Docs, Android, YouTube — the software people already use.
Read on Saurce →B2B Demand Gen Leaders Ditch MQLs for Sourced Revenue
Demand Gen Report's 2026 benchmark survey tracks how B2B teams are replacing MQL dashboards with sourced revenue, multi-touch attribution, and AI-driven workflows. The shift: measure AI ROI in content, scoring, optimization, and orchestration — not lead volume.
Read on MarketScale →💡 My Take
The 52-point gap is the story. 96% using AI, only 44% with adequate data infrastructure. That's not a tooling problem — it's an architecture problem. AI procurement decisions and data infrastructure decisions can't stay on separate tracks. The companies closing this gap first will pull away. Everyone else will keep running AI on bad plumbing.