AI use-case prioritization is the work of deciding which workflows to automate first, and in what order — before you build anything. A workshop produces thirty candidates that all look automatable. This is the scoring framework that turns that list into a ranked first five, chosen so the earliest wins are the safest ones: high-value, high-frequency work where the data is ready, the outcome is checkable, and a mistake is easy to undo.
The failure mode of an AI program is rarely a bad model. It is a bad first project. A team gets budget and enthusiasm, picks the workflow that sounded most impressive in the kickoff, and spends a quarter automating something rare, judgment-heavy, and irreversible — the exact profile of a project that produces a demo, then a rollback, then a quiet loss of nerve across the whole initiative.
The problem is that “which workflows should we automate” feels like a strategy question and is actually a scoring question. Everything looks automatable in a workshop, because a workshop strips away the two things that decide it: how often the work really happens, and what it costs to get it wrong. A candidate that saves an hour a week for forty people and one that saves a day a year for one person can look identical on a whiteboard. They are not the same project, and no amount of discussion sorts them — a rubric does.
This post is that rubric. It is a MOFU decision framework: you have decided to adopt an AI platform for business work and now have to choose where it earns its keep first. We will score candidates on six criteria, walk a worked example, and land on a single organizing principle — start reversible — that should shape not just what you pick but the order you ship it in. The criteria are deliberately platform-agnostic; where Insulin makes one of them concrete, the section says so with capabilities that exist today.
Why do most first AI projects pick the wrong workflow?
Most first AI projects fail because they optimize for impressiveness instead of for a favorable combination of value, frequency, and risk. The instinct is to automate the hardest thing, to prove the technology. The discipline is to automate the right thing first — often an unglamorous, high-frequency task — so the program banks a real win before it spends its credibility on a hard one.
Two biases push teams toward the wrong pick. The first is the novelty bias: the workflow nobody could do well by hand is the one people want the AI to do, precisely because it is hard — which is also why it is the worst place to start. The second is the average-value trap: leaders reason about total impact (“this would transform the department”) and ignore frequency, so a rare high-stakes task outranks a frequent modest one that would actually compound. Prioritization is the correction for both, and it works by forcing every candidate through the same numbered criteria instead of a meeting.
The six criteria for scoring an AI use case
Score each candidate 1–5 on six axes, then rank by the total. The axes are chosen so that the workflows that score well are, almost by construction, the ones an AI platform can take on safely and early. Use the same six for every candidate — the value of a rubric is that it makes two convincing pitches comparable.
| Criterion | The question it asks | Scores high when… |
|---|---|---|
| Business value | What does getting this done actually save or unlock? | It removes real, recurring cost — hours, cycle time, or missed revenue — not just effort someone enjoys offloading |
| Frequency / volume | How often does this work happen? | It runs daily or weekly across many people, so a small per-run saving compounds |
| Data readiness | Does the answer reside in documents or systems the AI can reach? | The source of truth is written down and connectable, not in someone’s head or a locked tool |
| Reversibility / risk | If the output is wrong, how bad — and how hard to undo? | A mistake is caught in review and costs nothing; nothing irreversible fires without a human |
| Clarity of “done” | Can you tell, objectively, whether it was done right? | ”Correct” is checkable against a source, a rule, or a number — not a matter of taste |
| Integration availability | Are the systems this touches actually connectable today? | The tools it reads and writes are already integrated, so it ships now, not after a connector project |
The rest of this section takes each criterion in turn. Read them as a filter, not a wish list: a candidate does not need a perfect score, but a 1 on reversibility or clarity of “done” should stop it from being a first project no matter how the other rows look.
1. Business value — recurring cost removed, not effort offloaded
The value question is what a completed instance of this work actually saves or unlocks, measured in something you could put on a slide. Score high for work that removes recurring cost: hours reclaimed, a cycle time cut, a follow-up that stops slipping through the cracks and losing revenue. Score low for work that is merely annoying — offloading a task someone dislikes feels valuable and often is not, because the task was cheap to begin with.
The trap here is counting effort instead of outcome. “This takes me forty minutes” is not a value statement; “these forty minutes happen sixty times a month and half of them are late” is. Value and frequency are separate axes on purpose — a task can be high-value per run and rare, or low-value per run and constant — and you need both numbers before you can rank anything.
2. Frequency and volume — the axis that makes small savings compound
The frequency question is how often the work happens, because a modest saving on a daily task beats a large saving on a yearly one. A workflow that runs many times a week across a team is where AI compounds: the per-run improvement is small, but it lands hundreds of times a month and the total is what shows up in the quarter.
This is the axis novelty bias erases. The impressive one-off — the annual analysis, the rare escalation — draws attention exactly because it is rare, and rarity is what makes it a poor first automation. The unglamorous truth is that the best starting workflows are usually boring: the weekly digest, the daily triage, the recurring check nobody enjoys. Rank frequency honestly and the boring winners rise to the top where they belong.
3. Data readiness — the answer has to live somewhere reachable
The data question is whether the information the workflow needs is written down and reachable, rather than living in someone’s head. An AI platform can only ground an answer in what it can read; a workflow whose “source of truth” is tribal knowledge or a leaver’s memory is not ready, no matter how valuable it is.
This is where grounding becomes a prerequisite rather than a nice-to-have. In Insulin, an agent answers from the knowledge bases you attach to it — your pricing, contracts, and runbooks — searched at query time, with the source document cited on each result, and Suger does not use your documents to train models. So “data-ready” has a concrete test: could you point the workflow at a knowledge base or an integration that contains the answer today? If the answer is only in a person’s head, the highest-value first move is often writing it down — which makes it ready for the next prioritization round.
4. Reversibility and risk — can you undo a wrong output cheaply?
The reversibility question is what a wrong output costs and how hard it is to undo — and it is the single most important axis for a first project. A workflow where a mistake is caught in review and thrown away costs you nothing to get wrong. A workflow that sends an email to a customer, writes to a system of record, or moves money is expensive to get wrong and slow to unwind. Start with the former.
This axis is where platform capability and prioritization meet. Insulin draws a hard line between reading and acting: reversible steps — reading a record, drafting a summary — can run freely, while consequential actions run through approval workflows in chat, where the agent “proposes a plan, you see it before anything runs, and you decide whether it runs at all.” That line is why a read-heavy workflow is a safe first use case even before you fully trust the system: the worst case is a draft you discard. We wrote about the mechanics of that gate in keeping a human in the loop; the point for prioritization is that reversibility is not fixed — an approval step converts a risky action into a reviewed one, which changes the score.
5. Clarity of “done” — you can check whether it was right
The clarity question is whether “correct” is objectively checkable — against a source, a rule, or a number — rather than a matter of taste. A workflow with a crisp success test (“does this figure reconcile,” “does this reply cite the right clause”) is one you can trust, monitor, and improve. A workflow whose output is judged by feel is one you can never quite sign off, so it stays a permanent pilot.
Prefer workflows where done is a citation you can open or a value you can reconcile. This pairs with grounding: because Insulin returns the source behind each answer, “was this right” becomes “open the citation and check,” which is a check a reviewer can actually perform. A candidate that scores a 5 on value but a 2 on clarity of done is a warning sign — you will build it, and then argue forever about whether it works.
6. Integration availability — the systems are connectable now
The integration question is whether the systems the workflow reads and writes are actually connectable today, so it can ship now rather than after a connector project. A brilliant workflow blocked on an integration that does not exist yet is not a first project; it is a roadmap item, and it belongs behind the ones that are unblocked.
Be concrete at the gate. List the systems each candidate touches and confirm they are integrated, because “we’ll connect that later” is how a two-week automation becomes a two-quarter one. This axis is also why scope matters: in Insulin you allowlist the integrations an agent may use, and a scheduled or event-driven job inherits that same scope — so a workflow you can express with today’s connections is one you can put on a schedule and leave running, while one that needs a system you have not connected simply cannot be built yet.
A worked example: scoring five candidates
Here is the framework applied to a realistic backlog for a fictional software company, Acme. Five candidates came out of the workshop; each is scored 1–5 on the six criteria, and the total decides the order. Scores are illustrative — the point is the method, and how it reorders an intuition.
| Candidate workflow | Value | Freq. | Data | Revers. | ”Done” | Integ. | Total |
|---|---|---|---|---|---|---|---|
| Weekly pipeline hygiene digest (flag deals missing a stage/owner) | 4 | 5 | 5 | 5 | 5 | 5 | 29 |
| Draft first-response to inbound support email (staged for review) | 4 | 5 | 4 | 5 | 4 | 4 | 26 |
| Reconcile marketplace payout report against internal records | 5 | 4 | 4 | 4 | 5 | 3 | 25 |
| Auto-send renewal quotes to customers | 5 | 2 | 3 | 1 | 3 | 4 | 18 |
| Draft the annual competitive landscape analysis | 5 | 1 | 2 | 3 | 2 | 4 | 17 |
The reordering is the whole lesson. The two candidates that felt biggest in the room — the annual analysis and auto-sending renewal quotes — land at the bottom. The analysis is rare (frequency 1) and its “done” is a matter of judgment (clarity 2); it is a poor first automation despite maximum value. Auto-sending quotes is high-value and reasonably frequent, but it fails on reversibility (1): an email to a customer with a wrong price is expensive and slow to unwind. Note that this one is rescuable — put the send behind an approval step and reversibility jumps, moving it up the list as a “draft and stage” workflow rather than an “auto-send” one.
The top of the list is unglamorous and correct. A weekly digest that flags pipeline records missing a stage, an owner, or a next step scores near the ceiling: high frequency, data that already lives in the CRM, output that is checkable at a glance, and — critically — it only reads and reports, so a mistake is a line in a digest someone skims, not an action that fires. That is your first project. It banks a real, repeatable win with almost no downside, and it earns the credibility you will spend on candidate three.
Start reversible: the principle behind the ranking
The organizing principle is start reversible: sequence your first projects so the earliest ones only read and report, and let acting come later, once the system has earned trust. Reversibility is weighted heavily in the rubric for a reason — it is the axis that most directly governs what a mistake costs, and early in a program you want the cost of every mistake to be near zero.
This maps onto a capability distinction, not just a preference. Reads and drafts are reversible: the worst outcome is output you discard. Writes and sends are not: once an email leaves or a record changes, undoing it is real work. So the safe adoption curve is to start with read-heavy workflows — a digest, a triage, a reconciliation that flags rather than fixes — where the AI’s job is to surface and summarize. Then introduce acting workflows behind an approval gate, so even the consequential steps stay reviewable until you have the run history to trust them unattended.
Prioritization and sequencing are two sides of one decision. The rubric tells you which five workflows; the start-reversible principle tells you the order — reversible reads first, approval-gated actions second, unattended automation only once a job’s run history has shown it behaving. Get both right and the program compounds: each shipped workflow makes the next one easier to trust, instead of each one spending credibility you have not yet earned. For the discipline of taking any single one of these across the line, the pilot-to-production checklist is the companion to this framework — this post picks the workflow; that one ships it.
Frequently asked questions
What is AI use-case prioritization? AI use-case prioritization is deciding which workflows to automate first, and in what order, before building anything. You score candidate workflows on value, frequency, data readiness, reversibility, clarity of “done”, and integration availability, then rank by total so the earliest projects are the safest, highest-return ones.
How do you choose the first workflow to automate with AI? Score each candidate 1–5 on six criteria — business value, frequency, data readiness, reversibility, clarity of “done”, and integration availability — and start with the highest total. In practice that is usually a high-frequency, read-only workflow like a weekly digest, not the most impressive one-off.
Why start with reversible AI workflows first? Because early in a program you want every mistake to cost near zero. Reads and drafts are reversible — a wrong output is discarded. Writes and sends are not. Starting with read-heavy workflows banks wins safely, and acting workflows come later behind an approval gate.
What makes a workflow a bad first AI project? Rarity, irreversibility, and a fuzzy definition of done. A workflow that runs once a year, sends something to customers automatically, or is judged by taste rather than a checkable source scores badly on the rubric — no matter how valuable or impressive it sounds.
How does data readiness affect AI use-case selection? An AI platform can only ground answers in information it can reach. If a workflow’s source of truth is written down in documents or systems the platform can connect to, it is data-ready. If the answer lives only in someone’s head, the first move is writing it down.
Can an approval step change how you score a workflow? Yes. Reversibility is not fixed. Putting a consequential action behind a human approval gate converts a risky auto-run into a reviewed step, which raises its reversibility score and can move an “auto-send” candidate up the list as a “draft and stage” workflow instead.
Takeaways
- Choosing which workflows to automate is a scoring question, not a strategy debate. Run every candidate through the same six criteria: value, frequency, data readiness, reversibility, clarity of “done”, and integration availability.
- The impressive one-off is usually the wrong first project. Frequency is the axis that makes small savings compound, so a boring high-frequency workflow beats a rare high-stakes one.
- Weight reversibility heavily. Start with read-heavy workflows where a mistake is a discarded draft, and introduce acting workflows behind an approval gate once the system has earned trust.
- Data readiness and integration availability are prerequisites, not scores to average away — a workflow whose answer isn’t written down, or whose systems aren’t connected, isn’t a first project yet.
- Prioritization picks the five; start-reversible sets the order. Reversible reads first, approval-gated actions second, unattended automation only once a job’s run history has earned it.
Pick your first five with the rubric, then start with the safest one: scope a read-only Insulin agent grounded in your own knowledge bases, put any consequential step behind human approval, and see the whole model in a live demo.
Sources
Primary sources for the platform rules cited above. Last verified August 20, 2026. Cloud providers change fees, eligibility, and program terms without notice — check the source before relying on a figure.
- Suger Insulin docs: Agents — Agents are scoped to an allowlist of integrations, and sensitive tool calls pause for human approval before execution.
- Suger Insulin docs: Jobs — Jobs run on a schedule or an event trigger and keep a full run history of what each run did.
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