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← Back to Content LibraryAI-Powered Demand Generation: Signal or Noise in 2026?
About the whitepaper:
By late 2025, 61% of B2B marketing budgets included a line item for AI-driven demand generation tools — yet conversion rates from MQL to closed-won declined for the third consecutive year (Forrester, Q4 2025)…
By late 2025, 61% of B2B marketing budgets included a line item for AI-driven demand generation tools — yet conversion rates from MQL to closed-won declined for the third consecutive year (Forrester, Q4 2025). This whitepaper argues that AI adoption in demand generation has outpaced operational readiness, creating a widening gap between technology investment and revenue outcomes. Using a four-stage maturity model, we diagnose where organisations stall and prescribe sequenced actions to close the gap. Written for VP-level revenue and marketing leaders, RevOps practitioners, and growth-stage B2B companies evaluating or scaling AI-native GTM motions.
In this whitepaper, you'll learn:
- How the B2B demand generation stack evolved from 2019 to 2026 and where AI created capability gaps rather than closing them.
- How to self-assess your organisation's position on the four-stage AI Demand Maturity Model and what each stage means for pipeline outcomes.
- Why MQL-to-SQL conversion rates decline even as AI adoption increases — and the specific configuration errors that drive this pattern.
- Which sequenced actions correspond to each maturity stage, from CRM data hygiene to autonomous revenue intelligence infrastructure.
- How to design a 90-day diagnostic and experimentation roadmap that separates AI signal from automation noise in your demand generation motion.