Agent programs often fail not due to a weak model, but rather because the problem they are meant to solve was not carefully identified or deemed worth solving. Getting this decision right is key in creating a successful framework.
AI use case identification The methodical approach of identifying areas where agentic AI can generate tangible business benefits precedes the decision on how to develop it. This process prioritizes actual business challenges over technology and categorizes each opportunity into one of three groups : deterministic AI, generative AI, or agentic AI : and prioritizes the resulting shortlist using an impact-feasibility matrix, optionally refined with a weighted scoring model when multiple candidates land in the same quadrant. A use case is a problem worth solving; a project is the work to solve it : The primary reason agent initiatives often stall after the pilot is due to the confusion between the two.
There are over a dozen frameworks available from top consultancies and cloud vendors, making it seem like the most difficult part of an agent program is choosing the right model or orchestration stack. However, the real challenge lies in organizations skipping the crucial step of identifying which business problem to focus the agent on.
An agent that is directed towards a target with low frequency, low impact, or a workflow that is not perceived as painful by users will not perform well, regardless of the quality of the underlying model. Precision in selecting targets is more effective than using advanced tools.
A use case assist claims adjusters in efficiently categorizing routine claims to reduce cycle time.
A project Skipping ahead to the project, whether it be choosing a framework, a vendor, or an orchestration pattern, without first identifying and validating the use case can result in teams creating an impressive agent that nobody requested, including everything that comes after: the architecture, data pipeline, integration work, and rollout plan.
Consistently following a discovery process ensures that a team stays focused on delivering value rather than chasing new ideas. Repeat the process for each business domain being evaluated.
One brainstorm session is unlikely to uncover the entire range of opportunities. Examining the same ideas through five different perspectives reveals potential use cases that may be overlooked with just one viewpoint.
Not every issue requires intervention from an agent. Properly categorizing a candidate from the beginning can prevent months of wasted effort on developing an incompatible system.
Deterministic AI Works best for specific workflows where consistency is key, such as fraud scoring, demand forecasting, and anomaly detection. Use it when reliability and traceability are more important than adaptability.
Generative AI Manages tasks involving content creation, summarization, and language that previously relied on human judgment, requiring less structured training data but more stringent oversight due to the subjective nature of evaluating its output.
Agentic AI Advancing beyond basic automation, the system autonomously orchestrates complex workflows across systems, enabling seamless resolution of customer issues, streamlined procurement processes, and efficient multi-system investigations that previously required extensive manual intervention.
Each function exhibits a unique cycle of minimal criticism and frequent tasks. This list serves as a starting point for the discovery session, not a comprehensive one.
Conversational resolution agents, sentiment-aware routing, always-on tier-one support.
Supply and inventory optimization, predictive maintenance scheduling, automated quality inspection.
Fraud and anomaly detection, automated compliance monitoring, invoice-to-payment matching.
Resume screening and shortlisting, workforce planning, engagement-signal analysis.
Anomaly detection, automated incident triage and response, infrastructure cost optimization.
Lead scoring, personalized content generation, dynamic pricing recommendations.
Department-specific use cases : The ticket triage of one team and the reporting of one desk provide rapid value as they are connected to one system, one owner, and one budget line, making them ideal for demonstrating the effectiveness of an agent program.
Cross-functional use cases A cohesive customer data agent and enterprise-wide knowledge assistant usually yield higher rewards, yet necessitate aligning data, budget, and ownership among teams that typically operate in silos. Prioritize departmental victories initially to pave the way for collaborative investments.
After creating the shortlist, map out each candidate on a two-dimensional graph. The matrix does not make decisions for you; it prompts necessary discussions to prevent wasting time on a futile use case.
A 2x2 matrix is a broad tool, often resulting in multiple strong candidates ending up in the same quadrant. Adding a weighted scoring model before or after the matrix can provide the precision that the matrix lacks.
| Scoring dimension | Question it answers | Typical weight |
|---|---|---|
| Business Impact | Revenue, cost, or risk moved, and by how much | 30% |
| Technical Feasibility | Does the required data, system access, and integration exist today | 25% |
| Data Readiness | Is the training and operating data clean, labeled, and available | 20% |
| Risk & Compliance Exposure | How much regulatory or reputational exposure does autonomy introduce | 15% |
| Time-to-Value | How quickly can a working pilot demonstrate the case | 10% |
In large logistics and operations organizations that take this process seriously, a common trend emerges: workshops throughout the business identify between thirty to over fifty potential use cases, including predictive maintenance and fully autonomous orchestration.
Navigating the impact-feasibility matrix reveals that only a few projects fall into the 'quick win' category, such as automated document processing, route optimization, and predictive maintenance. Focusing the initial investment on these areas, instead of spreading it thin across all ideas, is key to transforming a lengthy list of possibilities into tangible results within a single budget cycle.
The order in which things are done is just as important as what is chosen. Every new phase brings benefits and strengthens the decision-making skills required for the following phase.
Avoid the temptation to initiate all sections simultaneously. By utilizing a wave structure, small victories can provide funding and reduce the risk of subsequent strategic decisions.
Prioritize shipping use cases with the most impact and feasibility. Demonstrate the effectiveness of the operating model, set baseline metrics, and gain trust in the agents' output within the organization.
Allocate resources to enhance data pipelines and integrations to support complex, high-impact use cases. Implement governance measures such as monitoring, audit trails, and human-in-the-loop review.
Implement established strategies across departments, streamline common agent resources, and reassess the 'avoid / delay' quadrant as viability increases.
All the components in this guide - the discovery process, the three categories, the impact-feasibility matrix, the weighted scoring model - are designed to instill one key principle: identify the business problem before selecting the technology. Teams that adhere to this principle consistently deliver successful projects beyond the initial stage. Conversely, teams that fail to do so may have a flashy demo but struggle to secure funding for further development.
The AI strategy guides the organization's decisions on business domains, risk posture, and investment level, while use case identification determines the specific problems that AI or an agent should address first. Strategy determines where to focus, while use case identification determines what to prioritize in building.
Generative AI quickly generates content like text, code, and summaries with human oversight, while Agentic AI autonomously plans and executes multi-step workflows, interacting with various systems with minimal human involvement. For example, a generative use case could involve drafting a customer email, while an agentic use case might include reading and responding to emails, routing exceptions to humans as needed.
The grid is a 2×2 matrix where business impact is plotted on one axis and technical/organizational feasibility is plotted on the other. Each candidate use case is represented as a point on the grid. Use cases in the quadrant with high impact and high feasibility are prioritized as quick wins and are sequenced first. Candidates in the high-impact, low-feasibility quadrant are seen as strategic bets worth investing in. Low-impact items are either deferred or picked up opportunistically.
In a mid-size organization, between fifteen and fifty candidates are typically identified during the initial evaluation using all five discovery lenses. The focus at this stage is on broad coverage rather than precision, which comes later with the matrix and scoring model.
A combination of individuals experiencing the pain, assessing feasibility, and understanding regulations: process owners familiar with friction points, a data or platform expert providing honest feedback on feasibility, a risk or compliance representative for regulated domains, and an executive sponsor for final prioritization decisions.
Evaluate the feasibility of scoring with transparency, considering data availability as well as potential business outcomes. Many pilot projects fail due to overlooked data or integration challenges. Prioritize assigning a responsible individual before moving forward with a use case. Focus on achieving quick wins first to demonstrate value early on and secure funding for more complex projects in the future.
Incorporate the five-lens scan, three-category classification, and impact-feasibility matrix into your upcoming planning phase to ensure optimal decision-making before any agent is developed.
