A $20M Company Rarely Has a Marketing Problem
A mid-market company with good agencies, sharp hires, and full tools can still stall. The real gap is rarely tactics — it’s a missing marketing-leadership seat.
Practical strategies for integrating AI into your marketing, scaling with fractional leadership, and driving sustainable growth.
A mid-market company with good agencies, sharp hires, and full tools can still stall. The real gap is rarely tactics — it’s a missing marketing-leadership seat.
Campaigns run, the website exists, and something ships weekly — yet you’re still the de facto CMO. The tells you’ve outgrown whoever’s running marketing.
Fractional CMO cost, published without the hedging: real pricing models, what drives the number, and the exact tiers you can measure any quote against.
A German court held Google liable for false AI Overviews that called two real companies a scam. Here’s what the ruling means for how AI describes your brand — and what to do about it.
The five numbers a CEO should watch to know if marketing will produce revenue next quarter — and the vanity metrics that describe activity but predict nothing.
Fractional CMO first 90 days: the phase-by-phase roadmap that ships a working revenue plan, not a strategy deck — and what the CEO should see at each gate.
Fractional CMO, agency, or full-time CMO — which does a $10M+ company need? Your choice depends on the gap you’re closing: execution, leadership, or scale.
A fractional CMO is a senior marketing executive who leads your marketing part-time — owning strategy, the team, and results for a fraction of a CMO’s cost.
Fractional CMO hiring guide for $10M+ companies: what the role does, how it differs from an agency or full-time hire, what it costs, and when it fits.
Tabs open, tools subscribed, content shipping faster — and pipeline unchanged. The three-layer difference between AI theater and AI that produces results.
The four tells that AI adoption has outrun leadership, what that leadership function actually owns, and an honest look at director, CMO, and fractional paths.
Three levels of AI marketing measurement — efficiency, quality, and business impact — plus the vanity metrics to skip and the monthly review that keeps it honest.
Four stack layers ranked by actual return — frontier assistants, built-in AI, workflow glue, and point tools — and the evaluation rule that kills shelfware.
Voice drift, fabricated statistics, and volume without judgment — the three AI content failure modes, and the lightweight governance layer that prevents them.
Why hiring an AI specialist usually misfires at mid-market scale — and the patterns, prompt libraries, and practice your existing marketing team actually needs.
A prioritization scorecard for AI marketing use cases — impact, quality risk, setup effort, and dependencies — and why the boring workflows usually win.
The three marketing workflow families where AI integration pays first — content ops, nurture, and reporting — and the human quality gate that makes it work.
How $10M+ B2B companies integrate AI into existing marketing workflows, teams, and measurement — a three-layer framework for getting past tool adoption.
B2B buyers now research categories in ChatGPT and Perplexity before reaching your website. What the AI research phase is and how mid-market companies get visible.
How to measure AI visibility: track AI citations, share of AI voice, and AI referral traffic — and connect whether AI recommends your company to pipeline.
A practical AI readiness framework for B2B marketing teams: five dimensions — strategy, workflows, skills, data, governance — plus a quick self-assessment.
Why AI cites some B2B companies and ignores others — how E-E-A-T, earned media, named authors, and consistent brand facts build authority AI trusts.
How to structure B2B content for AI citations: answer-first sections, question-led headings, cited data, and FAQ schema that AI search can extract.
AI search reasons about entities, not keywords. How B2B companies use schema markup, consistent positioning, and sameAs links to become legible to AI search.
AI search vs. traditional SEO for B2B: what carries over (technical health, authority), what’s new (extraction, entity clarity), and where budget should go.
How AI assistants like ChatGPT choose which B2B vendors to recommend — the citation, corroboration, and entity-consistency signals that decide who gets cited.
How B2B companies get found and cited by ChatGPT, Perplexity, and Gemini — four pillars of AI search readiness, from crawl access and schema to authority.