Start by Solving One Problem
Finding the right initial use case is key to successfully deploying AI agents.
by Zhou (Jo) Yu
Leaders often talk about “adopting AI” like it’s a vision statement that redefines everything happening in an organization. In my experience, success is far more likely when leaders identify one discrete use case where an AI agent can deliver measurable results. This “wedge” creates momentum, builds internal trust, and uncovers practical lessons that make future deployments easier and more impactful.
My colleagues and I have gained experience helping small and large organizations deploy agents to handle specific, repetitive jobs like responding to customer inquiries, basic bookkeeping, and managing customer records. Automating just one time-consuming task can show leaders across an organization the value that agents can bring.
My startup, Arklex.AI, is helping major corporations use AI sales agents to increase revenue by handling customer interactions faster and more consistently than their human team could. We’re also working with a publicly traded robotics company to augment their devices’ ability to interact with humans.
The key is to find a clear need that’s suited to automation. That usually means an operation that relies on repetitive communication and well-defined goals, like communicating with customers who run into common issues. Once you manage to solve that initial problem, it’s much easier to extend the model to other internal processes and departments.
Given the significant progress in LLMs and other AI technologies, the real challenge in deploying an agent isn’t technical — it’s organizational. People are protective of their work. They don’t always want to write down what they know or change how things get done. That’s why success usually requires top-down commitment. In some cases, leaders had to insist: write down your process or risk being left behind.
If you’re preparing to bring agents into your organization:
- Start with a wedge. Choose a task that’s high-volume and well understood. We’ve seen successful implementations center on sales follow-up, customer service, and internal onboarding.
- Design for flexibility. Different teams and functions have different needs. Start with this in mind, and build templates that can work across teams with minimal rewiring.
- Plan for culture change. People need to see agents as support, not surveillance. Make productivity gains visible and reward the humans who made them possible.
- Enlist leadership. Cultural resistance is real. Signals from the top of the org chart can make or break implementation.
Using agents to solve one problem (especially if it drives revenue or brings down expenses) can create the momentum to roll out the technology across an organization.
Tomorrow, my colleague Vishal Misra will discuss the problems that agents can cause — and how you can mitigate that risk.
If you’d like to learn more about partnering with Columbia researchers working at the forefront of applied research in AI, visit the DAPLab website or contact co-director Zhou (Jo) Yu.
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