By Akshay Gupta, Associate Director – GenAI, Navi
A GenAI demo usually ends the moment the system produces an answer. In reality, that is when the real work begins. Someone still has to verify it, fix errors, decide what to do next, and take action.
This gap is critical when selecting use cases. An impressive, split-second response is useless if employees burn minutes fact-checking it. The only question that matters: does it actually improve the entire workflow? At Navi, our work across business analysis, coding automation, and customer interactions has heavily shaped how we answer this.
Start with a business problem
Retrieving knowledge, summarizing texts, and drafting are great capabilities, but they are starting points, not final business outcomes.
When an employee investigates a shifting business metric, finding data is just step one. They still need to compare trends, dig into root causes, and decide what to escalate. Our internal tool, Radar, which helps scores of teams access data and investigate these changes, tackles this broader need. The real opportunity is beyond just answering questions; it is reducing the cognitive load needed to actually reach a useful understanding.
The same logic applies to basic tasks. A document summary only matters if it helps complete a review faster. A drafted email only matters if it resolves a customer’s issue. Always start by identifying the underlying work that actually needs improving.
Evaluate the work that remains
Frequency and effort are your best initial filters. Saving a fraction of time on a task repeated thousands of times a week drives massive value. Infrequent tasks can also be worth the investment if the business impact is high enough.
Next, define what “good enough” looks like and look closely at the work left for the human in the loop. Take the coding automation workflow we are building: an agent helps clarify requirements, drafts a product requirements document, and generates a pull request for developer review. Generating the code is just one slice of the process.
The real test is how much human effort remains before the change can ship. If developers constantly have to clarify the AI’s requirements or rewrite its output, faster generation won’t speed up delivery. You have to measure the time spent reviewing and correcting against the time saved creating. Output is only valuable when someone can use it seamlessly.
Use GenAI where interpretation helps
Not every broken process needs GenAI. If the inputs are structured, the rules are rigid, and the outputs are predictable, traditional automation is often simpler and more reliable.
Routing a ticket based on a drop-down menu doesn’t require a large language model. But deciphering a customer’s rambling description of a problem? That’s where natural language interpretation shines.
The best workflows combine both. GenAI interprets the messy, unstructured request, while standard software applies the fixed rules and executes the action. Splitting the workload this way makes the entire system much easier to test and evaluate.
Let users and outcomes guide expansion
Pilots need to tackle real work with actual users. Throw incomplete requests, ambiguous questions, and complex escalations at it.
In our customer experience operations, we expanded our rollout only after observing concrete improvements in customer satisfaction. This bases the scaling decision on the actual experience delivered, not just a vanity metric like the volume of conversations handled.
For internal tools, look at retention: do employees actually return to the system? Does it genuinely lower their effort? For external systems, track core service outcomes alongside quality metrics. Always benchmark against the old process before going broad.
Don’t forget reliability. A brilliant answer loses its value if it times out during peak hours. Accuracy, speed, and uptime together dictate whether your team can actually depend on the tool.
Keep measuring after deployment
Lock in your baseline and success criteria before day one of the pilot. Track end-to-end completion time, output quality, review effort, and long-term adoption. Tie these numbers directly to hard outcomes: faster resolution times, increased team capacity, or lower cost per task.
When calculating ROI, bake in the costs of integration, monitoring, maintenance, and human oversight. Keep optimizing even if a project shows strong early returns; good returns often mask avoidable costs. Conversely, lower costs mean nothing if your quality deteriorates.
The best GenAI deployments eventually become ordinary parts of daily work. Teams spend less time hunting for files, reviewing bad drafts, or doing repetitive steps, and more time acting on high-value information.
That is the ultimate test for moving beyond the demo: whether the complete job gets done better, reliably, and at a cost that makes sense.