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Applied AI

Building an AI strategy for your industry

Start by writing one, 63% of firms have not. Inventory your proprietary knowledge, pick the highest-pain vertical use case, address the pricing-model implications, and run it through a 90-day roadmap rather than buying tools ad hoc.

4 min read/Written by Perry Luzier/Reviewed

From no strategy to a written one

A vertical AI strategy starts with your knowledge assets and a single high-value use case, explicitly handles the billable-hour question, and follows a phased roadmap. Skipping the written strategy is why most adoption stalls.

The 63% of firms without a written AI strategy are the ones whose adoption stays stuck at experimentation. A strategy does not need to be long, it needs to name the proprietary knowledge you will unlock, the one high-pain use case you will ship first, how you will handle the pricing-model implications, and the governance the vertical requires. Then run it through a disciplined 90-day roadmap. Firms that structure adoption this way report meaningfully higher ROI than those buying tools reactively.

Questions

Frequently asked questions.

How detailed does the strategy need to be to start?

A single page is enough to begin: the knowledge asset to unlock, the first use case, the pricing implication, and the governance guardrails. Detail grows as you ship. The point is to move from reactive tool-buying to a deliberate sequence, which is exactly what the 63% without a strategy are missing.

Want this built into your operation?

We install the systems described here as owned infrastructure. Start with a diagnostic of where your business actually loses time and margin.