.jpeg)
AI adoption in finance should always start small. After all, finance teams manage some of the most structured and sensitive data across a business. While the finance function involves recurring processes that are a natural fit for AI, it’s also a place where errors can have outsized consequences.
That’s why AI efforts should start with proof of concept. That means taking on AI pilot programs, or controlled experiments that test specific ideas in safe, measurable ways.
Done right, an AI pilot program can help finance leaders explore new possibilities without putting compliance, accuracy, or trust at risk. The key is to make pilots ambitious enough to demonstrate real value, but contained enough to protect the integrity of financial reporting.
Think of pilots as both technical and cultural experiments: opportunities to show that AI can enhance control (rather than threaten it) and build confidence in how AI supports (rather than replaces) human expertise.
This article offers a step-by-step framework for planning your first AI pilot program – from identifying the right use case and setting success criteria to enabling teams, choosing tools, and actually running your pilot.
Step 1: Define your ideal use case
The most successful AI pilot programs begin with a clearly defined, high-impact use case – one that addresses recurring pain points that consume significant manual effort or introduce preventable inaccuracies.
Start by mapping your existing workflows to see exactly where time and attention are being spent. Process mapping reveals inefficiencies and provides the baseline you’ll later use to determine how to improve your AI endeavors.
Early AI proof of concept projects should focus on tasks that amplify human work but don’t replace it altogether. Your team should build confidence in AI as a partner, not a threat. That means keeping judgment-based decisions firmly in human hands.
Overambitious AI pilot programs – such as trying to automate forecasting from end to end – often fail because they surface underlying data quality or governance gaps before delivering meaningful benefits. Instead, look for contained, repeatable processes where AI can show visible, measurable value.
Examples include using natural language generation to automate variance commentary, applying anomaly detection to flag irregular transactions, or enhancing cash flow forecasts by combining historical data with external signals. These use cases improve efficiency while also creating clear "before and after" moments that help finance leaders demonstrate credibility, show ROI through AI proof of concept, and secure buy-in for broader transformation.
Step 2: Set success criteria before starting
An AI pilot program without defined outcomes quickly drifts into endless experimentation – interesting, perhaps, but rarely actionable. Setting clear success criteria ensures that this work leads to a decision, whether that’s to scale, refine, or halt your efforts.
Begin by identifying a small set of quantifiable goals tied directly to business outcomes. These could include reducing manual workloads, improving forecast accuracy, shortening the close cycle, or improving the timeliness of financial insights. Define these measures early on, and agree on how you’ll track them. Dashboards, baselines, and before-and-after comparisons make results visible and credible to stakeholders who value evidence over enthusiasm.
Effective AI proof of concept initiatives evaluate success on two levels:
- Operational impact – measurable efficiency gains from automation, such as hours saved or processes accelerated
- Analytical improvement – enhanced quality of insight, accuracy, or decision-making confidence
Efficiency proves value, while accuracy builds trust. The key here is to balance each of these dimensions. Both are essential if AI is going to earn a permanent role in finance.
Equally important are your stop conditions – the points at which the pilot should pause or pivot. For example, if data preparation becomes more resource-intensive than the manual process it’s meant to improve, or if outputs create confusion rather than clarity, that’s a signal to reassess.
Prepare to document everything: the assumptions you made, the metrics you tracked, and the results you observed. These records will demonstrate progress and create an internal playbook for the next pilot, helping your organization mature its approach to AI in finance with each iteration.
With clear metrics and boundaries in place, you’re ready to move from theory to practice, empowering your team with the knowledge and structure they need to use AI effectively and responsibly.
Step 3: Enable teams with best practices
Even the most intuitive tools can fail without the right enablement. Finance teams need to understand how AI systems work, what they can and can’t do, and how to use them responsibly.
Begin by setting expectations. Pilot participants should understand that AI is probabilistic, not deterministic. It produces results based on patterns and probabilities, not hard-coded logic. For finance professionals accustomed to precision, that distinction is essential. Equip teams to “trust but verify” every output, reviewing AI-generated insights with the same rigor they would apply to a complex spreadsheet formula.
Enablement should go beyond tool training. It’s about teaching the “why” behind the system – how large language models interpret prompts, where hallucinations can occur, and how context improves accuracy. Empower your team with prompting best practices so teams understand how to craft inputs that guide models effectively. Encourage analysts to add relevant context, data references, and clear instructions in their prompts, since richer inputs almost always produce better outputs.
Build a short, structured feedback loop as part of the AI pilot program. Have analysts document when the model’s results were useful, misleading, or surprising. Those observations become your governance roadmap, helping you refine model performance and set clearer boundaries for future use.
Enablement should be considered one of your success metrics. An AI pilot program that improves efficiency but leaves users uncertain or distrustful isn’t a success. The goal is a confident, capable team that knows how to use AI as an accelerator rather than as a replacement for their expertise.
Step 4: Choose tools and partners wisely
With your use case defined and your team enabled, the next step is to select technology and partners that make experimentation safe and low-risk. The first AI pilot program isn’t the time for large infrastructure bets – focus on lightweight, flexible tools that let you test quickly and adjust as you learn.
Favor transparency over sophistication. If you engage vendors, request data-governance and security documentation upfront so you know where data resides, who can access it, and how it’s used for model training. Collaboration with internal IT or data-science teams can help ensure alignment with security standards, but you don’t always need to involve them deeply for an initial AI pilot program. Respect their guidance, but start small and keep it contained whenever possible.
Many finance platforms already include embedded AI capabilities, such as automated commentary, anomaly detection, or predictive forecasting. Activating these features within your existing systems can deliver early learnings in familiar workflows before you scale.
Once the right tools and collaborators are in place, it’s time to move into execution – running your pilot in a controlled environment that keeps the scope tight, the data secure, and the results measurable.
Step 5: Run the pilot in a controlled environment
The impact of an AI pilot program depends as much on its boundaries as on its ambition. Limit the scope to a single dataset, a single use case, and a single team. Narrow pilots make outcomes easier to measure and risks easier to manage.
Set a clear timeframe – ideally, a few months – to maintain momentum and signal that this is an experiment, not an open-ended project. A defined window keeps stakeholders engaged and forces prioritization of results over perfection.
Keep governance in check from day one. Track model versions, limit access, and log every output so you can easily trace how the system behaved and what decisions it informed. These habits will pay dividends later, when you’re ready to scale. If your organization has an internal audit or risk team, involve them early. Even informal check-ins can build confidence and prevent compliance surprises down the line.
When communicating progress, translate technical results into business terms. It’s not about whether the model is “accurate” per se – it’s about whether it made the process faster, improved visibility, or reduced manual rework. Framing results around measurable business impact helps secure ongoing support.
The outcome of a well-run pilot should be a documented before-and-after comparison showing how AI affected efficiency, accuracy, or timeliness.
Conclusion
The main goal of an AI pilot program is to generate proof of concept, not to deliver perfection.
When structured well, it can show you what’s possible while keeping risk contained. Each step – from identifying the right use case to choosing transparent tools to running the pilot in a controlled environment – is designed to build both confidence and capability within the finance function.
The lesson for finance leaders is simple: treat AI experimentation like any other disciplined financial process. Define your objectives, set measurable outcomes, document your results, and apply what you learn. Even small AI pilot programs can reveal big insights about data quality, governance, and where AI can drive the most value.
Over time, these experiments compound. Each AI pilot program strengthens your foundation for responsible AI adoption. By starting small and proving impact, finance teams can lead their business in thoughtful use of AI.
