1. What measurable result should this investment improve?
Define the baseline and target before approving the investment. The expected value of an AI project should be established before the work begins, not after the solution is delivered. Start by understanding the work being performed today. Where does the process slow down? What requires unnecessary manual effort? Where do delays, errors, or inconsistent decisions occur? Most importantly, what baseline will determine whether the investment made a meaningful difference? Without that baseline, even a successful implementation can be difficult to evaluate. The technology may work, but the organization may still be unable to show what changed or whether the result justified the investment. A request may also reveal:- A process that needs to be fixed before it is automated
- Data that is unavailable, unreliable, or insufficiently governed
- A capability already included in existing technology
- Multiple teams requesting similar functionality independently
Value should be defined before the build, not claimed after it.
2. What can be reused so the next project does not start over?
Consider what the project contributes to the broader AI portfolio, not only the immediate use case.
The first AI project may require significant foundational work. The tenth should not require rebuilding that same foundation from scratch.
Before approving an initiative, organizations should consider whether it will create reusable value beyond a single use case.
That may include:
- Shared data and integrations
- Governance and security controls
- Evaluation and measurement methods
- Workflow components
- Architecture patterns
- Lessons that improve future prioritization
Without reuse, an AI portfolio can become a collection of disconnected projects. Different teams solve similar problems independently, make overlapping technology investments, and repeat work already completed elsewhere.
A more disciplined approach considers both the immediate opportunity and what the project contributes to the wider portfolio. Each approved investment should make the next decision clearer, faster, or less costly.
The first AI project may require foundational work. The next one should benefit from it.
Move from AI ideas to an actionable plan
Better AI investment decisions happen before the build. Answering these two questions is an important first step, but organizations still need a consistent way to evaluate competing opportunities and decide what should move forward.
Curate’s AI Accelerator helps organizations assess readiness, define and compare potential use cases, and prioritize the opportunities most likely to deliver measurable value.
The result is a practical roadmap that identifies:
- What is ready to advance
- What requires additional foundational work
- Where process, data, governance, or technology needs to come first
- Which opportunities may not require a new AI build at all
