FASTR Framework: Five Checks Before You Fund an AI Bet
The FASTR Framework helps you screen a candidate AI or automation use case on five factors: Focused, Actionable, Scalable, Tangible, and Resilient. It is a go, no-go, or reshape filter, not a promise that a pilot will succeed in weeks.
Framework Card
- Name:
- FASTR Framework
- Goal:
- Filter a candidate AI or automation use case on Focused, Actionable, Scalable, Tangible, and Resilient before committing resources.
- Flow:
- Focused → Actionable → Scalable → Tangible → Resilient
- Best For:
- Choosing a first AI pilot from a long idea list; Defending why a vague platform bet should wait; Screening a use case for blast radius and measurability
Why it matters
AI idea lists grow faster than data, workflow entry points, and risk appetite. Leadership asks for transformation. Teams propose a “company brain.” Budget then lands on a project that cannot be described in one sentence, has no baseline, and would hurt the core process if the model is wrong.
FASTR is a way to make that mismatch visible before the spend. Each letter is a question you can fail. A failed letter is information: reshape the use case, or do not start.
What it is
FASTR is a five-factor filter used to choose AI-like projects that are narrow, buildable from what exists, able to grow later, measurable, and containable if they fail. Production teaching does not name a public academic source beyond a consulting origin. Treat the letters as the method, not a branded law.
Focused means one scene, one user, one goal. Actionable means data, systems, and a workflow entry point exist enough to start. Scalable means a small win can be reused or extended. Tangible means KPIs, a baseline, and a reporting rhythm. Resilient means blast radius, data sensitivity, and human oversight are acceptable.
The five are a checklist. They are not five sequential build phases.
How it works
Walk each candidate through the five factors. Record pass, fail, or reshape.
Focused
Can you name one user, one scene, and one goal in a sentence? Can a working loop be tried in a short cycle? “Build an AI platform” fails.
Actionable
Do manuals, tickets, or other data already exist? Is there an API or a workflow where a person already acts? Rebuilding a data lake first fails Actionable.
Scalable
If this works, can another team reuse it, or does it die as a one-person report tool? Small first is allowed. A dead-end custom tool scores weak here.
Tangible
Name one to three KPIs, the baseline, and how you will check them. “Better experience” fails.
Resilient
If the model is wrong, what breaks? Prefer internal suggestion with a human in the loop over unsupervised approval of money, safety, or legal outcomes. High-sensitivity data without controls fails.
Then make the call: try this use case, reshape it until weak factors are honest, or reject it. Do not average a red Resilient into a green total.
How it compares
When another lens fits better, or when you need a complementary view, these frameworks do different jobs.
| Framework | What it helps you see | How it differs from FASTR |
|---|---|---|
| ICE / RICE | Scored ranking of ideas (impact, confidence, effort, optionally reach) | Formula for many bets. FASTR is five fitness questions for an AI-like use case. |
| 80/20 Rule | Where results already concentrate | Empirical split. FASTR screens a candidate project. |
| MoSCoW | Must, should, could, will not | Demand labels. FASTR tests buildability and risk. |
| PDCA Model | Plan, do, check, act | Cycle after you have something to run. FASTR is the eligibility screen. |
The FASTR Framework is the lens for screening an AI-like use case on five factors. Other methods help when the question is idea scores, concentration, backlog labels, or an improvement loop.
When to Use This Framework
- Ideation workshops. Many sticky notes, no fitness test.
- Budget defense. A platform story is competing with a small, testable loop.
- AI risk screening. Someone wants the model to decide on a core, high-sensitivity process.
Example
A concrete example makes the structure easier to reuse when you are under uncertainty.
Example: HR policy bot versus “company brain”
A leadership offsite wants generative AI. Two candidates:
A. Policy question bot for HR partners, using existing employee manuals, suggesting answers a human sends.
- Focused: one user (HR partner), one scene (policy question), one goal (draft a cited answer).
- Actionable: manuals exist; the help desk already has an entry point.
- Scalable: the same retrieval pattern could later serve IT or finance FAQs.
- Tangible: time-to-first-draft, correction rate, tickets deflected (needs a baseline).
- Resilient: suggestion only; no auto-change of payroll; manuals are internal.
B. Company-wide AI brain that “knows everything” and acts in customer-facing systems.
- Focused: fails (many users, many goals).
- Actionable: fails (data lake rebuild).
- Scalable: claimed, not evidenced.
- Tangible: no baseline.
- Resilient: fails (unsupervised, high blast radius).
Implication: A is a FASTR candidate if Tangible gets a baseline. B should not be funded as a first bet. This is not a measured industry statistic.
Takeaway
What FASTR can help with
- Screening AI-like use cases before budget
- Making unfocused or unmeasurable bets visible
- Putting blast radius on the same page as value
- Creating a shared language across product, ops, and leadership
What FASTR cannot replace
- Numeric idea scoring. ICE and RICE rank unproven bets with scores. FASTR asks fitness questions for an AI-like project.
- Contribution analysis. 80/20 finds vital few in an existing set. FASTR does not measure past concentration.
- Backlog labels. MoSCoW marks must/should/could/won’t. FASTR tests readiness and risk.
- An improvement cycle. PDCA runs plan-do-check-act. FASTR decides whether the bet is eligible to run.
- Architecture, model eval, or a timeline guarantee. Passing FASTR does not design MLOps or prove two-to-four-week delivery.
Honest scope: FASTR structures a fitness screen. It does not replace the methods above.
Frequently asked questions
A serious fail on Resilient or Actionable should block or reshape the bet. Treating FASTR as an average of five vibes hides the red letter.
Production teaching is aimed at AI adoption. The same five questions can screen similar automation bets. The method is still the five factors, not a model vendor.
RICE scores reach, impact, confidence, and effort. FASTR asks whether the use case is focused, buildable from what exists, scalable later, measurable, and safe enough to try. You can still RICE several FASTR-passers.
A named use case, a pass/fail/reshape on each letter with case evidence, and a call: try, reshape, or reject. If the output only says “this is FASTR,” you do not have a screen yet.
No. It means the bet is eligible to try. Delivery, data quality, and adoption can still fail.