First written in March 2026 and updated in October 2026 to reflect the program as it stands.
I joined Right at Home in 2025 as its first full-time AI hire. Right at Home is a 480+ location in-home senior-care franchise network. In-home senior care is HIPAA-regulated, and each franchise owner runs their own business, so the compliance bar is high.
Much of the work was closing the gap between people using AI and people using it safely and well. This post covers how the company built its governance from zero, and what I learned.
Starting with Strategy, Not Tools
The first instinct in any AI initiative is to start building: pick a use case, sign up for an AI service, ship a prototype. That works for a prototype. In a HIPAA-regulated franchise network, where a confident wrong answer costs more than in most businesses, starting with tools before strategy is how a company ends up with a compliance problem.
We began with the organization's first AI strategy document, a structured narrative memo covering vision, principles, a phased roadmap, and an execution model that separated corporate-track initiatives from franchise-track enablement. It went through multiple versions with executive stakeholders before it became the plan.
Decisions over the following months traced back to it: which tools to vet, which products to build, which training came first. When priorities were questioned, the strategy was the reference point.
Establishing the AI Governance Council
I stood up the Enterprise AI Governance Council, recruited its members from across the company's business units, and gave it the frameworks it works from: SOC 2 and HIPAA-aligned compliance standards and a vetting process for AI tools and vendors. Then I handed the council to a teammate, who has run it since.
The design choice that mattered most: the line is drawn by the vendors the company vets, not by a policy document. That let the company open up safe AI experimentation for employees instead of publishing restrictions people quietly work around. Teams can't adopt tools ad hoc, but they have a clear, predictable path to approval.
Adoption Is the Deliverable
Governance alone tells people what they cannot do. The goal was people using AI well within the guardrails, so the program treats adoption, not the launch, as the deliverable.
An AI Advocate in Each Corporate Function
The enablement program seats an AI advocate in each corporate function, so each function has its own voice in the program. The advocates are the first point of contact for AI questions in their function and the main channel for surfacing new use cases. A small AI team can't personally support every team, but it can support a group of advocates who translate between the AI strategy and how their function works day to day.
A Standing Schedule of Enablement Sessions
Alongside the advocates, the program runs a standing schedule of enablement sessions. The sessions teach, and they also surface new use cases and concerns that feed back into the governance framework and the intake pipeline.
The franchise network has its own enablement track: franchise education sessions, readiness for the corporate coaches who introduce new products to franchise owners, and the guides, FAQs and onboarding that move offices from awareness to everyday use.
The AI Opportunity Pipeline
Alongside the council, we built an intake and scoring pipeline that ranks franchise requests, so the program can decide which become platform investments rather than one-off builds. When someone asks "why aren't we doing X?", the answer is a score rooted in the framework.
The program's first two products, the analytics agent and the knowledge agent, are covered in Building an Enterprise AI Strategy from Zero.
Lessons Learned
I treated governance as a product. The people submitting tools for review are its users, and process improvements are its backlog.
Start with the user's reality. Governance decisions were designed around the constraint that franchise owners are running businesses and corporate staff have full jobs; nobody is there to study AI.
The strategy document is alive. It went through multiple versions as we learned what worked and what didn't; later versions changed the execution model, and the vision stayed the same.
Outcomes
The program produced:
- Two AI products for corporate staff: the plain-English analytics agent and the Microsoft 365 Copilot knowledge agent, both in production
- Governance in place: a strategy document, a governance council, a vetting process and an intake pipeline for new AI requests
- A council that runs on its own: stood up, then handed to a teammate, and still running
- Adoption as the deliverable: an AI advocate in each corporate function, a standing schedule of enablement sessions, and teams across the corporate office building and running agents of their own
When the next tool arrives, the company has a process to evaluate it, approve it and teach people to use it.
The two products above were later joined by a third, Connect, a voice AI sales-coaching platform for the franchise network.