Which AI use case
should you build first?
Score candidate AI projects on business value, feasibility, data readiness, risk, and implementation effort — then rank them with weights that match what matters to your organization right now.
See a quick-wins quadrant chart and a prioritized backlog instantly. No account is required, and nothing is sent to Dwayo unless you choose to save, export, or request a review.
Scoring weights — adjust to match your priorities
| AI use case | Value (1-5) | Feasibility (1-5) | Data readiness (1-5) | Risk (1-5) | Effort (1-5) | ROI score |
|---|
Quick-wins quadrant — value vs. effort
Prioritized backlog
Every roadmap has more
good AI ideas than capacity.
Most teams don't lack AI use cases — they have a backlog of plausible ones, each pitched with its own enthusiasm and its own blind spot. Without a shared scoring method, prioritization quietly defaults to whoever argues loudest, whichever use case is easiest to demo, or whichever executive asked most recently.
A structured score doesn't remove judgment. It makes the judgment calls explicit — this is the business value we assigned, this is the risk, this is why effort was rated high — so the backlog can be reviewed and defended instead of just announced.
The five dimensions
behind the score.
Each use case is rated 1-5 on five independent dimensions, then combined using your weights.
The expected impact if the use case works as intended — revenue, cost reduction, retention, risk reduction, or a capability competitors don't have. Score against a specific, measurable outcome, not a general sense of importance.
How achievable the use case is with current models, tools, integrations, and team skill. A high-value idea that depends on capabilities the team hasn't built yet should score lower here, not on value.
Whether the data the system needs already exists, is accessible, is reasonably clean, and is labeled or structured well enough to use. Many AI projects stall here long after the model choice was settled.
The consequence of the system being wrong, combined with how exposed that failure is — customer-facing, financial, safety-related, or regulated use cases carry materially more risk than an internal drafting tool.
A relative estimate of engineering effort, integration complexity, and organizational change required — not a staffed project plan. Use it to compare candidates against each other, not as a committed estimate.
How the
score is calculated.
Three steps from five ratings to one ranked list.
inverted = 6 − rating.score = weighted sum / (5 × total weight) × 100The score is a ranking aid, not a business case. Use it to decide what to scope next, not to replace the scoping itself.
Why two "good ideas"
can rank very differently.
Internal knowledge base search assistant
- Value: 3 — saves employee time, no direct revenue
- Feasibility: 5 — mature RAG pattern, internal users tolerate rough edges
- Data readiness: 4 — documentation already exists and is mostly current
- Risk: 2 — internal-only, low consequence if wrong
- Effort: 2 — well-understood scope
- Result: High score — a strong, low-risk first use case.
Autonomous customer refund agent
- Value: 5 — removes a slow, costly manual process
- Feasibility: 2 — requires reliable tool use and strict guardrails
- Data readiness: 3 — order and policy data exists but is spread across systems
- Risk: 5 — financial, customer-facing, hard to reverse if wrong
- Effort: 4 — approval workflow, audit logging, and testing all add scope
- Result: Lower score despite high value — a strong second-phase candidate once guardrails exist, not a first use case.
What this ranking
doesn't account for.
A sound starting order, not a finished roadmap. The score doesn't capture:
- Dependencies between use cases (shared infrastructure, sequencing)
- Current team capacity and competing non-AI priorities
- Strategic alignment with company-level goals
- Regulatory or compliance review timelines
- Stakeholder and change-management readiness
- A scoped engineering estimate for cost and timeline
- Unknown unknowns that only appear once a pilot starts
When to run
this prioritizer.
- Building an AI roadmap from a list of candidate ideas.
- Deciding what to pilot first with limited engineering capacity.
- Reconciling competing AI proposals from different teams.
- Re-prioritizing after a use case's feasibility or risk changes.
- Presenting a defensible AI investment order to leadership.
- Revisiting the backlog each planning cycle.
Before you take this
to leadership.
How is the ROI score calculated?
Each use case is scored 1-5 on business value, feasibility, and data readiness, and 1-5 on risk and implementation effort (where 5 is worst). Risk and effort are inverted, then all five dimensions are combined using the weights you set, producing a 0-100 score. The default weights favor value and feasibility, but every weight is adjustable.
What is the quadrant chart showing?
It plots business value against implementation effort. Use cases in the upper-left are high value and low effort — typically the best starting points. Use cases in the lower-right are low value and high effort, and usually belong at the bottom of a roadmap.
Should every AI use case be scored the same way?
No. Weights should reflect what matters to your organization right now. A team building credibility for an AI initiative may weight feasibility and data readiness heavily; a team under competitive pressure may weight business value more.
Does this tool estimate engineering cost or timeline?
No. Effort is a relative 1-5 rating, not a staffed estimate. Use the LLM Cost & Capacity Calculator for inference cost, and get a scoped engineering estimate for timeline and staffing once a use case is shortlisted.
How many use cases should I score at once?
Most teams get useful signal from 5-15 candidate use cases. Scoring far fewer rarely reveals a clear priority order; scoring far more often means the list hasn't been filtered to realistic candidates yet.
Does Dwayo see the use cases I enter?
The scoring runs in your browser. Your use cases are only shared with Dwayo if you choose to save a result, export the backlog through the lead-capture flow, or request a review. See our privacy policy.
A ranked backlog is a starting point
for scoping, not a finished plan.
Once your top use cases are clear, a Dwayo engineer can help scope the first one — architecture, data readiness, and a realistic estimate — before it goes on a roadmap slide.