A long list of ideas is not a plan

Once a team starts looking for AI opportunities, the list grows fast. Customer support, invoice processing, contract review, scheduling, reporting, and a dozen smaller tasks all look plausible on a whiteboard.

The failure mode is not a shortage of ideas. It is treating every idea as equally ready to build, so the project that gets picked is whichever one a senior stakeholder mentioned most recently rather than the one that will actually pay off first.

Score every candidate on the same dimensions

A defensible ranking needs a small, fixed set of criteria applied to every candidate the same way. Four dimensions cover most of what matters for an operational workflow: business impact, feasibility, data readiness, and risk.

Score each candidate on each dimension using a simple scale, then compare totals across the list. The point is not precision. It is forcing every idea through the same filter instead of relying on whoever argues for it most persuasively.

  • Impact: cycle time, cost, error rate, or volume affected
  • Feasibility: whether the workflow steps are well-defined enough to automate
  • Data readiness: whether the needed evidence is accessible and current
  • Risk: consequence of a wrong or delayed output, and who is accountable for it

Feasibility usually comes down to data, not the model

Two candidates can look similarly impressive on impact and still differ enormously in how quickly they can ship. The difference is almost always whether the required information already lives somewhere reachable, current, and permissioned for the workflow to use.

Before scoring feasibility, list the systems and documents each candidate needs to touch, who owns each one, and how the workflow would get access. A candidate that depends on a system nobody has agreed to connect yet should score lower than one that only touches data the team already has.

Risk and oversight belong in the score

The GAO's accountability framework for AI in federal agencies puts governance first, calling for entities to set clear goals and engage stakeholders before a system is built, rather than adding oversight after the fact.

The same logic applies to a business roadmap. A workflow that only drafts a low-stakes internal summary can tolerate more automation with lighter review than one that writes back to a customer record or approves a payment. Score that consequence explicitly so a high-impact but high-risk candidate is not accidentally sequenced ahead of the review process it will need.

NIST's AI Risk Management Framework offers a useful reference point for categorizing that risk consistently across candidates, rather than judging each one informally as it comes up.

Sequence the roadmap, do not just rank it

The candidate with the highest total score is not automatically the one to build first. A smaller, well-scoped workflow that ships in a few weeks and proves the review and monitoring pattern is often worth more than a bigger project that takes a quarter to reach its first real user.

Use the scores to shortlist three or four strong candidates, then sequence within that shortlist by dependency and speed to a working handoff. The goal of prioritization is a roadmap the team will actually follow, not a spreadsheet that only proves the exercise happened.