What Makes a Use Case High Value? A Framework for Prioritizing AI Investments

AI investments demand not just ambition but strategic clarity. Innovation teams must decide which use cases warrant investment by balancing four key dimensions—impact, feasibility, governance, and time to value. This framework helps filter out noise and focus on opportunities that deliver measurable results.

Business Impact and Scale

A high-value use case delivers tangible outcomes, such as revenue growth, cost reduction, improved retention, or competitive differentiation. In June 2025, RSM announced a $1 billion investment over three years in agentic AI platforms aimed at automating workflows across tax, audit, and consulting¹. This scale underscores a purposeful strategy tied directly to enterprise performance, not experimentation.

Technical Feasibility

Big ideas flounder without infrastructure. Technical feasibility examines data pipelines, model readiness, integration capabilities, and organizational adoption. Goldman Sachs’ GS AI Assistant is a textbook example—initially rolled out to 10,000 staff and now launching firmwide with support for document summarization, report generation, and analysis². That rollout reflects infrastructure maturity—secure pipelines, LLM access, and user training—all of which are in place to support scale.

Governance and Risk

High-value AI must be responsible AI. Risks range from bias and regulatory issues to model drift and data privacy. In 2025, banks, including Goldman Sachs, JPMorgan, UBS, and Citi, piloted automations for junior-level analytical tasks under clearly defined governance models³. By selecting low-risk, high-volume workloads and embedding human oversight, these banks built trust and compliance into AI from day one.

Time to Value

Speed builds momentum. Use cases that deliver within months, not years, to attract stakeholder attention and funding. A balanced innovation portfolio should include rapid pilots to demonstrate value alongside longer-range transformational efforts. Quick wins build credibility—and fund bigger ambitions.

Scoring Matrix for Decision-Making

To operationalize this framework, teams can score each use case across the four dimensions, like so:

Dimension	Score (1–5)	Rationale
Business impact		Projected uplift or savings
Technical feasibility		Data readiness, model maturity, and infrastructure
Governance maturity		Oversight, auditability, and compliance
Time to value		Estimated timeline to delivery

A combined score of 15 points or higher indicates that a candidate is eligible for fast-track investment. Those scoring lower might need refinement or reconsideration.

Data Native™ in Application

7Rivers built the Data Native™ model specifically for this type of evaluation. It starts with structured, reusable data pipelines—supporting feasibility. It embeds compliance and audit controls—addressing governance. It focuses on outcome-driven architecture—aligning with impact. And it uses modular accelerators to accelerate delivery—shortening time to value. With this approach, pilots are not isolated but integral to a scalable, secure, and intelligent data ecosystem.

From Pilot to Strategic Program

Start by identifying high-value use cases, then launch pilots equipped with outcome tracking, feedback loops, and governance dashboards.. Utilize Data Native™ foundations to scale successes from pilot to enterprise solution. Teams that implement this model transition from fragmented AI experimentation to coherent, portfolio-level AI capability.

If your organization seeks a structured approach to evaluating AI use cases, embedding governance early, and scaling value through a Data Native™ architecture, 7Rivers is ready to partner. We help you move from idea to measurable business impact, with systems that flow with your strategic goals.