The Art of the POC: Turning AI Experiments into Enterprise Wins
Reflecting on my experience with running AI experiments...
Financial organizations are under pressure to explore AI, but a proof of concept (POC) should be more than a flashy demo, vendor sales pitch, or quick click-through. When executed well, POCs provide clarity on whether a tool can create measurable value and be trusted by the business users. Done poorly, POCs can turn into nightmares.
Define Success Early
Start by articulating what “good” looks like. Be specific—don’t just say “improve efficiency.” Instead, define a target outcome like reducing claims processing time by having the AI conduct an initial review of claims documents. If the goal is productivity, quantify it in terms of hours saved or reports automated. These definitions allow business stakeholders to evaluate outcomes objectively instead of debating opinions after the fact. Without a clear benchmark, a POC risks drifting into an expensive experiment with no clear finish line.
Understand Your Data
AI is only as strong as the data it ingests. Before spinning up models, invest time in cleaning, labeling, and categorizing the dataset you plan to use. Decide whether to work with synthetic test data—which protects customer data but may lack nuance and lead to model over fitting—or with carefully deidentified production data, which is closer to reality but requires stronger governance. Data lineage, consistency, and labeling matter here: garbage in, garbage out certainly applies in AI.
Secure the Environment
In the world of finance, we operate under strict regulatory and reputational pressure. POCs should be built in a sandbox environment with tight access controls and regular testing for vulnerabilities. It’s important to bring compliance, risk, and security teams into the process early instead of treating them as a late-stage hurdle. A POC is not just about proving technical feasibility—it’s a chance to demonstrate the organization can innovate without compromising its fiduciary responsibilities. Involving internal teams in governance builds organizational muscle, deepens expertise, and makes the company more nimble when it’s time to pivot
Change Management
One of the most overlooked aspects of running a POC is managing expectations. Business leaders often assume AI = instant transformation. This believe can be disconnected from the expectations held by business end users. A POC, by definition, is messy—it surfaces limitations as much as possibilities. Misalignment here can create frustration and kill momentum even if the tech shows promise.
Successful teams communicate clearly that the POC is about learning, and not always deploying at scale. It’s important to set expectations on what the tool can and cannot do today, and they actively coach testers to focus on potential value rather than perfection. Managing this human side of adoption is just as critical as managing the technical build.
Design the Test Structure
Scope is everything. Select one or two high-potential use cases and design a test that maps the workflow clearly: input → model interaction → output → user action. Define a control group and a test group so you can compare results. Assign real users—claims analysts, customer service reps, or fraud investigators, for example—to provide live feedback. This structure ensures the POC reflects day-to-day work rather than abstract lab conditions.
Measure Both Hard and Soft Metrics
Quantitative measures should include token usage, latency, number of queries, escalation rates, and hallucination percentages. These metrics help calculate cost, reliability, and risk exposure. But don’t stop here. Collect qualitative feedback directly from testers: Did this save you time? Did it make your job easier? Did it introduce new frustrations? Encouraging candor surfaces friction points that raw data can’t capture. Gather a few quotes from business users—real testimonials help managers and decision makers get a clear sense of how their teams actually feel about the AI tool.
Assessing Success
A POC should graduate if three conditions are met: it delivers measurable improvements against the success benchmark, it operates within the organization’s risk guardrails, and end users express a strong desire to keep using it. If all three are true, you don’t just have a proof of concept—you have a proof of value. That’s the real signal it may be worth implementing in production.