76% of CRM users say less than half their data is accurate. Over 40% of businesses have abandoned a CRM entirely. But for small and solo service businesses, the problem isn't bad data or poor training — it's that the entire $90 billion CRM category was designed for a business model you don't have.

The data itself is still accurate, but the meaning behind those actions is changing.

My favorite part of this piece is buried near the end: nobody can actually measure whether GEO works, but they're spending anyway. We've officially entered the "we don't know if this does anything but everyone else is doing it" phase of the AI search cycle. Welcome back, early 2000s SEO energy.

Executives set the vision and teams test tools, but transformation happens in the middle. Equip managers to drive AI-powered workflow change.

Turns out the gap between demo and deployment isn't the AI — it's that nobody documented the tacit knowledge your best employee carries around in their head.

Embedding AI into marketing workflows promises big efficiency and performance gains — but you’ll need the right ROI models to justify the investment.

As AI moves from pilot to production, expect data debt to become the limiting factor — not model capability. The practical implication: audit your data lineage and real-time accessibility before committing to the next AI initiative. Organizations that treat data infrastructure as a pre-condition rather than a cleanup project will compound returns on AI spend; those that don't will keep adding cost complexity on top of broken pipelines.