OnTrac AI - AI Strategy Playbook

Purpose

The AI Strategy Sprint is a repeatable engagement designed to help organizations move from vague interest in AI to a clear, prioritized plan of action.

It creates shared understanding, surfaces real opportunities grounded in day-to-day work, and produces a roadmap leadership can confidently execute.

Core Question

"Given our goals, systems, and constraints, where should we apply AI next?"

Inputs

Input Type	Description
Business goals	Growth, efficiency, speed, risk reduction, customer experience
Operating reality	How work actually gets done today
Systems landscape	ERPs, data warehouse, reporting tools, workflows
Team constraints	Time, skills, trust in data, change tolerance

Outputs

Output	Description
Opportunity inventory	Concrete, workflow-level AI opportunities
Prioritized roadmap	Ranked initiatives with sequencing logic
ROI framing	Why each item matters and what it unlocks
Executive narrative	Clear story leadership can align around
Next-step options	Pilots, resourcing models, and ownership paths

How the Sprint Works (At a Glance)

Step	What Happens
Align	Confirm goals, scope, and success criteria via kickoff
Listen	Interview functional leaders and teams
Observe	Map workflows, pain points, and data gaps
Synthesize	Normalize notes into a common framework
Score	Weigh opportunities objectively
Decide	Build a focused, phased roadmap
Present	Deliver a crisp executive readout

Design Principles

Principle	Why It Matters
Business-first	AI follows value
Workflow-grounded	Opportunities start with real work
Tool-agnostic	Avoid premature vendor or platform lock-in
Honest tradeoffs	Explicitly say no to low-leverage ideas
Momentum-oriented	Always end with clear next steps

Who This Is For

Role	Value
Executives	Confidence and clarity on where to invest
Functional leaders	Relief from manual work and ambiguity
IT & Data	A sane, prioritized demand signal
Operators	Fewer spreadsheets, less busywork

What Makes This Different

Typical AI Effort	Strategy Sprint
Idea-driven	Evidence-driven
Tool-led	Problem-led
Broad	Narrow and specific
Politically ranked	Objectively scored
Hard to act on	Designed for execution

End State

At the end of the sprint, the organization has:

A shared understanding of where AI fits
A small number of high-confidence bets
A roadmap that balances quick wins and foundations
A clear path from strategy to execution