Every use case selection methodology eventually reduces to two questions: how much is it worth, and can we actually do it? The value-feasibility matrix — a 2×2 grid that maps those two dimensions — is the most widely used tool for prioritizing agentic use cases, and it works well for exactly the reason that simple tools usually work well: it forces an explicit conversation about both dimensions rather than allowing the team to optimize for only one. But the 2×2 also contains a trap that swallows a disproportionate number of early agentic programs: the temptation to populate the portfolio entirely with high-feasibility, moderate-value use cases, leaving the high-value, harder-to-execute work for a "later" that rarely arrives on schedule.
Defining Value for Agentic Work
Value in the context of agentic use cases has multiple dimensions that are not always captured by a single ROI calculation. Efficiency value — the reduction in labor cost or time-to-completion from automating a task — is the most straightforward to quantify and the one that most business cases lead with. Quality value — the improvement in the accuracy, consistency, or comprehensiveness of outputs compared to the human baseline — is harder to quantify but often more strategically significant, because it reflects the agent's ability to do things that humans cannot do reliably at scale: checking every document against every policy, monitoring every data feed in real time, maintaining perfect consistency across thousands of parallel decisions. Speed value — the reduction in the latency between a trigger event and a completed response — matters enormously in time-sensitive domains like financial trading, cybersecurity incident response, and customer-facing service delivery. And strategic value — the extent to which the use case builds organizational capability, generates proprietary data, or creates competitive advantage that compounds over time — is often the least quantified but the most important dimension for long-horizon portfolio planning.
A common mistake in value assessment is to anchor on the cost of the human task being automated. An agent that replaces a task that costs $50,000 per year in labor is creating at most $50,000 per year in value — and usually less, because the human was also doing other things in the time spent on that task. But an agent that enables a task that was previously too expensive to do at all — continuous monitoring across a data corpus that would require fifty analysts to cover, personalized communication at a scale that would require a thousand writers — may create value that is an order of magnitude larger, because it is not replacing existing capacity but creating new capacity.
Measuring Feasibility
Feasibility for an agentic use case is a composite of three assessments: technical feasibility (can current agent capabilities reliably complete the task at the required accuracy level?), data feasibility (does the organization have the data the agent needs, in the format, quality, and accessibility required?), and integration feasibility (can the agent be connected to the systems it needs to interact with, within the security and compliance constraints that apply?). All three dimensions must pass for a use case to be truly feasible, and failure on any one of them typically blocks progress on the others: a technically capable agent that cannot access the data it needs cannot demonstrate its capability; a well-connected agent that generates unreliable outputs cannot build the organizational trust it needs to operate with reduced human oversight.
Technical feasibility is the dimension that is most often overestimated by teams excited about new model capabilities. The right question is not "can the model do this task in a demo?" but "can a production agent do this task reliably enough, across the full distribution of inputs it will encounter, to meet the accuracy threshold required by the business case?" That threshold is almost always substantially higher than what is demonstrated in a prototype, because production inputs are messier, more varied, and more adversarially structured than the carefully curated examples that make demos look impressive.
The Easy-First Trap
The value-feasibility matrix defines four quadrants: high-value/high-feasibility (obvious prioritization), high-value/low-feasibility (worth working to enable), low-value/high-feasibility (quick wins, but not strategically important), and low-value/low-feasibility (avoid). The easy-first trap is the tendency to populate the portfolio with low-value/high-feasibility use cases and call them quick wins, while deferring the high-value/low-feasibility work indefinitely. This produces a program that generates activity without transforming the business.
The trap is reinforced by organizational dynamics that are entirely understandable: quick wins are politically important for sustaining the program's budget and momentum, data readiness and integration work is unglamorous and takes longer than expected, and the teams responsible for high-feasibility deployment often have no direct incentive to solve the readiness problems that would unlock high-value use cases. Breaking out of the trap requires explicit portfolio governance — a rule that at least a defined fraction of the portfolio must be allocated to high-value use cases even when they are currently low-feasibility, with active investment in the readiness improvements needed to raise their feasibility score.
"A portfolio of fifty low-risk agent deployments does not produce the same organizational transformation as ten high-value ones. Volume of deployment is not a proxy for strategic impact."
The Scoring Methodology
Translating the 2×2 into a practical prioritization tool requires a scoring methodology that is simple enough to be applied consistently but rigorous enough to be meaningful. A workable approach scores each dimension on a 1–5 scale, with explicit rubrics for each score level. For value: 1 is a labor cost reduction below $50K annually with no strategic component; 5 is a capability that does not currently exist and creates measurable competitive advantage. For feasibility: 1 means multiple critical gaps in data, integration, or technical capability; 5 means all components are available and the task has been demonstrated to work at the required accuracy in a realistic test environment.
The composite score — value multiplied by feasibility — ranks use cases for near-term prioritization. But the prioritization should not be mechanical: a use case that scores 4×2 (high value, low feasibility) may deserve prioritization over a use case that scores 2×4 (low value, high feasibility) if the organization has a credible plan to close the feasibility gap within a defined timeframe. The scoring methodology creates a common vocabulary for the portfolio discussion; the judgment about which cases to prioritize remains with the governance council.