Why pilots matter for AI in mission-driven work

Many mission-driven organizations we’ve worked with hesitate to implement AI because they recognize potential risks to their operations, mission, and reputation. Guidance online is generic, and doesn’t recognize the priorities and circumstances of mission-driven organizations.

Pilots are a key way that mission-driven organizations can safely test and evaluate AI implementations in their precise circumstances. A lot can go into a well-executed pilot! Join us for a series of blog posts about how to select, design, evaluate, and scale one of these closely scoped tests!

We’ll start today by reviewing what an AI pilot looks like, their benefits, and a preview of the next posts in the series!

What is an AI pilot?

In this series, a pilot is a time-limited, scoped experiment in a specific workflow with:

1. A clearly defined task or use case (for example: draft first versions of case notes, summarize public comments, triage email).

2. Agreed-upon success criteria and metrics (for example: time saved, error rates, changes in staff satisfaction, equity-related indicators).

3. Documented guardrails and human oversight (for example: humans always review outputs before they affect clients; no automated denial of benefits).

4. A plan for feedback and evaluation (for example: staff log issues, a working group reviews metrics, community advisors react to impacts).

5. A pre-committed decision path (for example: “scale,” “revise and retest,” or “retire” based on the evidence).

This lines up with broader digital innovation work that stresses staged validation. AI is just a particularly sensitive case where you also have to validate fairness, explainability, and legal fit.

What can a pilot do for you? 

Piloting before full implementation allows you to:

• Manage risk
  You can explore value while you protect residents, clients, and staff from avoidable harm.

• Learn in your own context
  AI tools behave very differently on your data, in your workflows, with your constraints. Pilots give you evidence grounded in your exact context.

• Build trust and capacity
  When you involve staff and community in pilots—as testers, reviewers, and advisors—you treat them as partners rather than subjects. That can support legitimacy and help surface issues you can iron out before they disrupt operations at scale.

• Avoid “pilot purgatory” and random experiments
  Digital experimentation can drift into chaos if nobody defines clear questions, metrics, or decision rules. A well-designed pilot, by contrast, starts with a decision in mind: “If we see X, we scale; if we see Y, we stop or redesign.”

Pilot series preview:

1. Choosing a workflow or task to pilot

By the end of this post, you’ll have a shortlist of candidate workflows and a simple rubric to choose the first one.

2. Deciding what to measure and setting up measurement

You’ll leave this post with an evaluation plan that will define success for your pilot.

3. Designing the pilot rollout: who tests, for how long, and how they log feedback

By the end of this post, you’ll have the tools you need to design the specifics of your AI pilot. 

4. Evaluating pilot results and deciding what happens next

You’ll get a template for a short pilot retrospective memo you can reuse across projects.

5. Scaling (or not): moving from pilot to practice

If your evaluation indicates a “go” decision, this post will give you best practices for scaling up. 

Looking forward to working through pilots with you!

—

LLM disclosure: I asked ChatGPT 5.1 thinking to draft a post based on an outline I wrote and my approach to piloting from my manuscript draft. I did request that it try to follow my tone, but I my edits still centered around that. I expect that the terse tone of the outline and book conflict enough that it didn’t get a good read on me this time.