One of the most common patterns across organizations is this: A surge of excitement around AI, several pilots launched quickly, impressive demos, and then very little that actually makes it into day-to-day use. The solution quietly stalls, never fully integrating into how the organization operates. This isn’t a technology problem. It’s a use case selection and adoption problem.
AI use case determination is one of the most consequential decisions organizations make early on, because it sets the trajectory for everything that follows: adoption, trust, scale, and long-term value. The goal is not to build something flashy. It’s to identify use cases that are actively used, continuously improved, and capable of expanding into broader opportunities as confidence and fluency grow.
A sustainable approach to AI starts by meeting the organization where it is today, the AI fluency across the organization, not just within technology teams, and progressing deliberately as maturity increases. This framework maps AI use cases to organizational maturity levels.
Level 1: “I Know Aabout AI”
Start with Productivity-Focused Use Cases
For organizations that are new to AI, the best place to begin is not with large, transformational initiatives. It’s with productivity-improving use cases that help people do their jobs better, faster, or with less friction.
At this stage, use cases should answer two simple questions:
- Does this meaningfully improve productivity?
- Can we demonstrate ROI through time saved or experience improved, even if there’s no direct revenue impact?
Copilots, intelligent search, and summarization tools work well here—low risk, immediately useful, and they normalize AI as a partner.
ROI needs to be reframed here. Measuring early AI success purely through revenue often leads to disappointment. Instead, focus on efficiency gains, reduced cognitive load, faster turnaround times, and improved employee experience. These early wins build confidence and create a foundation for deeper adoption. The real value at this stage isn’t just productivity. It’s AI fluency.
Good AI note:
This is also a critical stage to introduce ethical and responsible AI usage. Organizations should actively train people on what responsible use looks like, monitor how tools are being used, and provide clear, practical examples of risky or inappropriate behavior. Policies alone are not enough; education and real-world context matter.
Level 2: “I Use AI Fluently in My Daily Work”
Embed AI into Mature, Well-Understood Processes
Once basic AI familiarity is established, organizations can begin embedding AI directly into business processes. This is where many teams feel tempted to jump straight to complex or high-visibility workflows. Instead, start with stable, mature processes.
The strongest AI use cases are anchored in mature, well-understood processes that are stable, repeatable, and already producing consistent outcomes. When a process is well understood, ambiguity is lower, which makes AI outputs easier to validate, explain, and trust.
Human-in-the-loop design is essential here. AI should be paired with domain experts who deeply understand the process and its edge cases. These humans validate outcomes, catch anomalies, and ensure decisions remain fair and contextual. Rather than removing accountability, AI reinforces it.
This approach helps people feel secure as owners of AI-enhanced processes, not threatened by them. Gradual adoption allows humans to understand how agents behave, how to improve them, and how to use them more effectively over time.
Good AI notes:
- At this stage, the human who owns the process should also be accountable for the AI agent. That means upskilling them technically enough to train the agent, provide feedback, and continuously improve outcomes. Ownership drives trust.
- Before committing to any use case here, ask: “If this AI works perfectly, does it still matter to the business two or three years from now?” AI investments tied to processes that are being sunset or deprioritized rarely deliver lasting value. Sustainable AI reinforces enterprise priorities, not short-lived departmental needs.
Level 3: “AI Drives Measurable Business Impact”
Advancing to Net-New and Transformational AI Products
As organizations gain confidence and fluency, they often look to AI to power entirely new products or significantly enhance their business capabilities. This is natural progression, but complexity escalates here. The same discipline still applies. New AI-driven products must align clearly to organizational goals, and ROI must be thoughtfully defined, even if those metrics evolve over time.
One of the most effective patterns at this stage is decomposition. Instead of building one large, monolithic AI solution, break the end-to-end experience into multiple smaller AI agents. Each agent should perform a specific task exceptionally well and either feed into or consume outputs from other agents.
This is where bounded context becomes powerful. Each AI agent operates within a clearly defined domain. This reduces dependencies, simplifies validation, and allows agents to be enhanced, repaired, or retired independently. It also makes it far easier to test and trust each component.
Good AI notes:
- Human-in-the-loop design remains critical here, especially during training and early production. Humans validate outputs, guide refinement, and ensure alignment with real-world expectations. Over time, this creates a scalable ecosystem of AI capabilities rather than a single brittle solution.
- Maintaining lineage across agents is equally important. Understanding how inputs are transformed, what outputs are produced, and who uses them are all foundational to trust. This is where governance-enabled AI capabilities play a powerful role.
Principles That Apply at Every Level
While use case selection sets the direction, delivery determines whether AI succeeds or fails.
Change management must start from day one. AI initiatives rarely fail because the model didn’t work. They fail because people weren’t ready for what the model would change. Adoption is not a downstream activity. It is designed from the beginning. Assessing stakeholder readiness, data fluency, and trust early makes an enormous difference. Involving users in defining success metrics, validating outputs, and shaping workflows creates ownership rather than resistance.
Ethics and responsibility must be built in, not bolted on. Ethical considerations should surface during use case vetting, not after deployment. What decisions will this AI influence? Who could be unintentionally impacted? Governance plays a critical role here, not as a constraint, but as an enabler that provides clarity on ownership, accountability, and lineage. Ethical AI doesn’t end at go-live; outcomes must be continuously monitored as conditions evolve.
Summary: Design for Production, Not Experimentation
One of the biggest risks organizations face is accumulating a collection of disconnected AI pilots. Without intentional design for production, AI remains an experiment rather than an enterprise capability. Sustainable AI use cases are embedded into workflows and decision chains. They start narrow, validate with humans, and expand deliberately. Trust compounds over time when AI proves to be reliable, explainable, and aligned with real business needs.
The difference between AI that stalls at POC and AI that scales depends on the rigor of use case selection, the discipline of progression, and the commitment to people-first design. Trustworthy AI is built long before the first line of code: through alignment, decomposition, governance, and an unwavering focus on how people actually work.
