Prepared following our conversation

A First 90 Days:
Helping LCS Become AI-Native

A hypothetical plan for the VP, Data & AI role, built from public information, and meant to show how I'd approach the work. I'd validate and refine every piece of it with your team in week one.

David Pedelty, 25-year multi-unit operator who builds AI hands-on. Not the deepest data scientist in the room; the leader who ties AI to business outcomes and gets the field to actually use it.

Why I'd be effective, fast

What I bring to this specific role

Business outcomes, not tech for its own sake

25 years of P&L leadership. I read AI through occupancy, margin, and conversion, the same way LCS already frames its data wins.

I build agentic AI hands-on

My own infrastructure, an MCP server, and 40+ deployed projects, so I can direct the frontier, not just buy it.

I've led a technology transformation before

As COO of an EHR company (acquired by ChiroTouch), I led developers, sys admins, and support through a client-server → SaaS migration in a HIPAA-regulated environment.

I get adoption in the field

My whole operating career is making systems work for busy, non-technical people across many locations, the make-or-break for AI-native.

How I read where LCS is

You've done the hard foundational work

Which is exactly why this role is about leverage, not starting over.

You stood up a data science team and Insight Advantage a couple of years ago, with real adoption, real reporting, and algorithms already lifting Visit-to-Move-In conversion. You're scaling through the Vi merger, and your 2026 wellness model uses AI to give staff time back for real resident connection. The next leap, genuinely AI-native with agentic AI across 140+ communities, needs a leader who ties every model to business value and drives adoption in the field. That's the gap I'd fill.
The plan

The first 90 days

Three phases: earn context, set the strategy with early proof, then deliver a measured win and a repeatable engine.

Days 0–30

Listen, learn, map the landscape

  • Meet leadership, IT, the data & governance teams, and the field operators
  • Go deep on Insight Advantage: what drives decisions, where it stalls
  • Audit data model, platform stack, governance, in-flight AI, team skills
  • Confirm the business outcomes AI should move; learn the Vi timeline

By Day 30: a shared current-state + opportunity map, and an agreed definition of "AI-native" for LCS.

Days 31–60

Strategy + first quick wins

  • Draft the enterprise Data & AI roadmap, tied to business value
  • Prioritize use cases: agentic workflows, pricing/amenity optimization, move-in acceleration, staff-time-back
  • Stand up responsible-AI guardrails with the governance director
  • Ship one or two quick wins; map the team's growth into agentic roles

By Day 60: an approved roadmap, governance in motion, quick win(s) delivered, first agentic pilot scoped.

Days 61–90

Prove, operationalize, scale-ready

  • Launch the first agentic pilot; measure move-ins, margin, labor, time returned to residents
  • Establish the intake → prioritize → deliver → measure operating rhythm
  • Build the field adoption plan across 140+ communities
  • Align IT on next-gen AI architecture; fold in the Vi communities

By Day 90: a proven pilot with measured ROI, a scalable operating model, and a 12-month plan the exec team can rally behind.

How I'd operate

Guiding principles

Business outcomes first. Every initiative ties to move-ins, occupancy, margin, labor, or resident experience.
Build on what's working. Extend the foundation you've built — don't rip and replace.
Adoption is the real work. A capability nobody in the field uses is worth nothing.
Responsible by design. Governance, security, and ethics move with delivery, not after it.

I'd welcome the chance to pressure-test this with your team

This is a starting point, not a finished answer. The real plan gets built with the people who know LCS best. If it's useful, I'm glad to walk the team through it.

david.pedelty@gmail.com 217-766-1560 davidpedelty.com