Covers AI applied to growth strategy, marketing operations, team transformation, AI adoption frameworks, ROI measurement, and organizational change. Includes annual state-of-the-field summaries synthesizing survey data, AI marketing roadmap frameworks, B2B vs. B2C comparison explainers, and guidance for marketing leaders building the internal case for AI investment. Excludes channel-specific tactics (those belong in Content Marketing, Advertising, SEO, or Sales). Serves marketing managers, directors, and CMOs who need strategic framing and benchmarking data, not just tool tutorials.
14 pieces
A structured practitioner reference for marketing professionals producing or overseeing AI-generated content — organized by content type rather than tool, covering automation viability ratings, tiered QC requirements, and known failure modes for each of six distinct content categories.
A structured reference guide covering how AI applies to marketing analytics — what tasks it handles reliably, where it fails, which tool categories exist, and what practitioners need to know before adopting it in their analytics workflow.
A structured reference guide covering how AI applies to email marketing — what tasks it handles well, where it underdelivers, which tool categories exist, and what practitioners need to know before adopting it in their email programs.
A structured reference guide covering how AI applies to paid search — what it handles well, where it fails, which capabilities are mature versus experimental, and what practitioners need to know before handing over control.
A structured reference guide to Gartner's AI marketing technology forecast data for 2025, covering enterprise adoption rates by function, spend benchmarks, and what the numbers actually mean for practitioners making budget and tooling decisions.
A structured reference guide covering how AI is applied in email marketing automation and personalization — which capabilities are mature, which are experimental, and what failure modes practitioners need to manage before adopting AI-driven email workflows.
A practical reference on how AI hallucinations surface in marketing content workflows, what the real consequences look like, and what operational controls actually reduce the risk.
A structured reference guide covering how AI is applied in paid search advertising — from mature automation like Smart Bidding to experimental generative ad formats — including known failure modes, control trade-offs, and what practitioners need to understand before relying on platform AI.
A practical reference on FTC AI disclosure requirements as they apply to marketing content — covering endorsement guides, material connection rules, synthetic media, and what compliance actually looks like in practice.
A structured reference guide covering how AI is applied across B2B demand generation channels — from intent data and content syndication to ABM personalization and pipeline scoring — with honest notes on what's mature, what's still rough, and where the failure modes live.
AI-generated ad creative sits in a genuine legal grey zone — no clear copyright ownership, unresolved training data liability, and platform policies that shift faster than court rulings. This is a practical reference for marketing teams who need to understand what the risks actually are before scaling AI creative production.
A structured reference guide covering how AI is applied across the programmatic display channel — from audience modeling and bid optimization to dynamic creative and brand safety — with honest assessments of what's mature, what's still unreliable, and what marketers need to know before committing to AI-driven workflows.
Surveys consistently show that consumers respond differently to content labeled as AI-generated — but the direction and magnitude of that effect depends heavily on content type, brand category, and how the disclosure is framed. This analysis breaks down what the data says and what it means for marketers deciding whether and how to disclose AI use.
A four-phase execution framework — Assess, Prioritize and Pilot, Scale, Govern and Measure — designed for marketing directors and VPs of Marketing who have run AI pilots but need a structured, growth-tied system to move from fragmented experimentation to end-to-end AI-driven outcomes they can defend to CFO and CEO stakeholders.
