The market for agentic artificial intelligence (AI) is poised for explosive growth, but with this opportunity comes immense pressure. The selection of early use cases is fraught with difficulty as decision-makers try to balance potential impact with ease of implementation.
Teams that have been through AI adoption before know the importance of proving future viability by demonstrating a strong return on investment (ROI) in early projects. Failure to reach acceptable thresholds can stall the overall adoption journey, and the company may miss its competitive-advantage window. To that end, here is a five-step checklist that adopters of agentic AI should go through to avoid implementing the wrong use cases.
The 5-Step Framework for Selecting Agentic AI Use Cases
1. Start with the Problem, Not the Technology
Agentic AI shows great potential in demos supported by carefully chosen data. Do not be tempted to conduct a proof of concept for a scenario the enterprise is unlikely to encounter. Instead, start by thinking about pain points—frustrating workflows, imperfect data access, or key decisions being delayed by manual processes. Repetitive work that calls for organisational intelligence and time-consuming documentation is perfect for agentic AI.
To help narrow the search, look for highly skilled employees who dedicate significant time to tedious activities or workflows slowed down by the need to access multiple systems. Identify information gaps and silos that lead to poor decision-making; AI agents can bridge these gaps through data aggregation and analysis.
2. Categorise the Value Proposition
Not all AI agent use cases deliver the same kind of value. They typically fall into one of three categories:
- Process Automation: These use cases streamline and add efficiency to a single workflow. They can be implemented with a human agent monitoring their performance initially, then moved to full automation.
- Worker Augmentation: These use cases empower employees by equipping them with virtual assistants. They can be implemented on day one because employees retain the ability to veto actions.
- Intelligent Business Chains: These are the most advanced use cases, reinventing entire processes and enabling new business models. As such, they must be implemented more cautiously.
3. Assess Your Technical Readiness
Before proceeding, ensure that your current data access supports the use case, that business processes are well-documented with clear success metrics, and that built-in governance capabilities are on hand to maintain control and visibility. Jumping into implementation is inadvisable without first assessing data access and quality.
4. Prioritise by Impact vs. Effort
To choose the right use case, organisations must balance impact with ease of implementation. Prioritise use cases with a high confidence in potential ROI, implementing straightforward ones first and earmarking more complex ones for the future. Assess ROI by asking questions about time saved, costs reduced, risks mitigated, and revenue earned.
5. Pick One, Perfect It, and Then Proceed
For the purposes of building trust among corporate stakeholders, do not try to implement more than one agentic AI use case at a time. Pick one, implement it, and evaluate it thoroughly. Your team can then apply the lessons learned to future projects. Moving carefully builds buy-in from key decision-makers, who become more likely to back future projects.
The Unifying Principle: Governance Is Not an Add-On
Beyond the five steps, a single principle must guide your entire strategy: governance cannot be an afterthought. It should be defined and understood by all participants from the outset and updated as lessons are learned. Stakeholders from the business must be part of the governance framework and consulted on all decisions relevant to their expertise; the redesign of a process can never be optimal without input from the people who know it best.
To ensure non-technical experts can contribute effectively, consider using visual tools they can understand. This principle extends to the tools that show what agents are doing. Strong governance requires transparency, not just for building trust, but for ensuring regulatory compliance. Long-term success will only come about through careful adherence to this framework.
