Creating a Data (+ AI) Roadmap
A data roadmap is your strategic plan for how your organization will collect, manage, analyze, and responsibly use data and emerging technologies over the next 1–3 years.
It’s the bridge between where your systems are today and where they need to be to support your mission—whether that’s automating manual reports, improving program insights, or exploring AI tools that actually make your team’s work easier.
A roadmap helps you:
Align your team around shared data and AI priorities
Stop chasing tools that don’t serve your mission
Build data and AI capacity thoughtfully, not in crisis mode
Show funders credible, transparent impact data
Make confident decisions about when (and when not) to use AI
Turn insight into action—consistently and sustainably
The Data + AI Roadmap Framework
1. Assess Your Current State
Start with an honest look at where you are today. What data are you collecting? How is it stored and protected? Who uses it, and how could AI support (not replace) that work? Identify the gaps between what you have and what you need. Tools like the Data Maturity Assessment from data.org are a great place to start.
2. Define Your Destination
What questions do you need data to help answer? What does success look like in 1–3 years? If exploring AI, define clear use cases: automating manual tasks, generating insights faster, or improving data quality.
3. Prioritize Your Initiatives
You can’t do everything at once. Rank projects by impact and feasibility. Quick wins (like cleaning up a messy spreadsheet or improving data entry) build momentum for bigger lifts (like piloting an AI-driven tool for text analysis or automating routine reports).
4. Sequence Your Steps
Some steps must happen before others. You need clean, organized data before you can responsibly use AI. You need well-defined metrics before you can build dashboards or predictive models. Map dependencies and set realistic timelines.
5. Assign Ownership and Resources
Who will lead each initiative? What budget, time, and tools do they need? If you’re exploring AI, who ensures ethical use, transparency, and privacy?
6. Build in Check-ins
Schedule quarterly reviews to track progress, celebrate wins, and adjust priorities as your needs—and technologies—evolve.
Common Pitfalls to Avoid
Starting with tools instead of strategy. That shiny AI app won’t help if your data foundation isn’t strong.
Perfectionism. Your roadmap doesn’t need to be perfect—it needs to be useful. Start with what’s most urgent and iterate.
Forgetting about people. Data and AI are only as effective as the humans using them. Budget time for training, process changes, and building trust.
Going it alone. Involve staff across programs, leadership, and partners early. Data and AI strategies are strongest when they’re shared.
Ready to Build Your Roadmap?
Creating a roadmap doesn’t have to be overwhelming. We help mission-driven organizations build practical data and AI strategies that are designed for your capacity, budget, and goals.
