It sucked that a PE-backed, multi-site physical therapy group had already proven AI could work, but every win was isolated.

Clinicians were using an AI scribe, vendor bots were handling prior auths, and a few people were pushing AI hard on their own. But most use was out-of-the-box chat that didn't know how the company worked, and the most capable automation belonged to vendors, moving at the vendors' pace. None of it had a clear, measurable path to EBITDA, so there was no way to tie scattered AI use back to the business objectives. " Underneath that were the real problems. Growth was capped by clinician capacity, not patient demand. Reporting was stitched together by hand from several systems. Every new AI idea raised the same question with no good way to answer it: build it, buy it, or wait on a vendor? And most good ideas ran into the same wall: the data that mattered was locked inside systems that weren't connected.

We ran a fixed-fee, 6-week AI and automation diagnostic (our "Blueprint") with a small leadership core team and weekly check-ins. 1. Weeks 1–2: start from the P&L and work back. Deep discovery across the business, tied out to the primary business objectives, mostly profit and loss. From there we worked back to the levers that move those numbers, and into the operational areas with the most leverage: high headcount, or people with direct influence on revenue. A two-day onsite with the CEO and functional leaders kicked it off, followed by interviews and "reverse demos" where their team walked us through every core system live. 2. Weeks 3–4: go deep on the operations that matter. We dug further into those high-leverage operations, mapping system and data dependencies and where things really stand today. Early on, we also put prototypes and concepts in front of leaders to react to, so the future state was concrete before the roadmap was written. 3. Weeks 5–6: turn it into a roadmap. We built a full opportunity inventory scored on impact, effort, and readiness, wrote business cases for the top initiatives, and sequenced a 12-month roadmap with build, buy and vendor recommendations. What came out of it: - A plan for AI-native operations across the whole company. Not everything needed a project. One track was a broad adoption strategy built on champions: a hands-on champion in each department who levels up, then builds shared skills and connectors that bring AI into the department's real work. An executive owner keeps it pointed at the business. It turns one-off wins into how whole departments operate. - An inventory of opportunities, each with a clear path: buy it, ask a vendor for it, or build it. It wasn't a list of build projects. More than half of the opportunities started with something they already owned, like a vendor roadmap ask, a configuration change or a process fix. That gave leadership specific, well-informed asks and real leverage going into vendor conversations. We recommended building only where they couldn't wait on a vendor, where the work was unique to them, and where owning the IP was worth it. Three builds came out of that, each with a business case: - Real-time operating feedback for the highest-leverage people (up to $4M/yr projected). A copilot that moves the people with the most direct influence on revenue from reacting to data to acting on it, with AI surfacing the 2-3 most important things they can do today to move the business. - An agent harness for autonomous operations (up to $3M/yr projected). A foundation for fully automating the processes that don't need a human in the loop. We laid out how to build the agent harness and how to keep adding operational automations to it over time, one workflow at a time. - A talent database and dashboard. One company-owned view of a large pool of potential clinicians, so recruiters can find, prioritize and follow up with the right candidates. Built to be AI-native from the beginning with MCPs into their AI chat interfaces. - A clear path through the data and integration dependencies. We mapped the data issues to clear and the integrations needed to support each initiative. That set the sequence: start with what isn't blocked, and push for system access and data readiness in parallel. It gave them a path from isolated, disconnected AI use to connected AI, from individuals working with AI in their own systems all the way to agents running on the harness. The PE sponsor told us they've had "nothing but tremendous feedback" on the work.

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