Start from workflows, pains, data, skills, assets, and constraints.
Identification starts with evidence
Generic lists begin with what AI can do. Evidence-backed identification begins with what is true about the company: where work slows down, which decisions depend on scarce judgment, what data is reachable, where customer pressure is building, what constraints apply, and which capabilities competitors cannot easily copy.
Recommendations appear only with evidence
Foundry’s opportunity library spans automation, decision support, analytics, customer work, finance, governance, software, physical operations, research, compliance, growth, and new offerings. A recommendation appears only when the company evidence supports it. A support assistant, for example, should not outrank a painful core workflow when there is no unique support knowledge or proven support problem.
Prioritization is not a single ROI guess
Each candidate carries written reasons across three core axes:
- Impact — does the linked pain, frequency, cost, or strategic pressure matter?
- Feasibility — are the required data, skills, systems, ownership, and controls available?
- Moat — would the opportunity compound proprietary data, scarce expertise, reusable capability, distribution, or switching cost?
Quality, risk, and timing checks sharpen the decision without hiding the underlying evidence.
Sequence before funding
The roadmap distinguishes quick wins from larger bets. It also keeps measurement, data access, test coverage, safety controls, and other required preparation visible. Missing evidence becomes a specific interview or validation question rather than a vague warning.