How to Start an AI Pilot Program (Step by Step)

Although artificial intelligence (AI) appears to be everywhere, recent studies show that around 95% of AI pilots fail to deliver measurable business value. Using more accessible tools like ChatGPT and Gemini, many organizations have adopted a haphazard ‘throw AI workflows at the wall and see what sticks’ strategy. This opens the door not only to failure but also to wasted resources, compliance risks, and even employee distrust.

Launching a successful AI pilot takes effort, but it’s more achievable than you think. In this guide, we’ll show you how to build a pilot that’s safe, measurable, and built to deliver—covering everything from success metrics to choosing the right provider, while helping you sidestep the pitfalls that sink most projects. The goal: make sure your pilot is among the 5% that truly succeed.

What is an AI pilot project?

An AI pilot project is a small-scale trial implementation of an AI tool or process, usually to test how well it solves a specific problem. The benefit of running a pilot program is that you’re able to measure the feasibility, functionality, and business impact before rolling out on a larger scale.

Depending on the use case, pilots can be run in a simulation or sandbox environment, or isolated to one team or department. Keeping the new AI process separate from business-as-usual is vital not just for lowering risk, but for measuring results.

In short, an AI pilot program should be:

• Measurable: Outcomes can be tracked with clear success metrics.
• Low-risk: Limited scope, with safeguards to prevent major disruption.
• Targeted: Focused on a specific, well-defined business problem.
• Scalable: Designed so that successful results can be expanded later.
• Practical: Solves a real pain point, not just a “shiny tool” experiment.

Step 1: Identify a business problem AI can solve

Don’t start with the tech. Start with the business challenge. Too many AI initiatives fail because a shiny new tool lands in the market promising the world, and teams try to shoehorn it into their work without asking if it solves a real problem.

The key to getting started with AI is starting small. Is there something that regularly soaks up hours of employees’ time, but requires no human skill? Looking for quick wins by automating these repetitive, rules-based pain points.

To identify the low-hanging fruit, ask yourself these questions:

• Is the task repetitive, rules-based, and frequent?
• Are the inputs and rules clear and structured (not ambiguous or judgment-heavy)?
• Does it have a measurable outcome (e.g., time saved, errors reduced, faster turnaround)?
• Would automating it lower the risk and free up employees for higher-value work?
• Can it be piloted safely with minimal disruption to core operations?

Examples: simple AI pilot programs

If you’re struggling to identify the low-hanging fruit in your organization, here are some ideas to get you started.

General business ops

• Processing routine invoices and expense claims
• Automating customer FAQ responses
• Routing support tickets to the right team
• Generating recurring reports or dashboards
• Approving standardized requests (e.g., PTO, travel).

HR Operations

• Screening resumes for basic qualifications (e.g., years of experience, certifications).
• Scheduling interviews across time zones.
• Processing onboarding documents (ID verification, benefits enrollment).
• Updating employee records in multiple systems.
• Generating standard HR reports (headcount, turnover rates).

Payroll

• Calculating payroll across multiple jurisdictions.
• Detecting duplicate or missing entries.
• Validating overtime, deductions, and bonuses.
• Cross-checking tax rates and contributions.
• Reconciling discrepancies between systems.

Compliance and risk

• Monitoring regulatory changes across regions.
• Flagging incomplete compliance documentation.
• Tracking license and certification renewals.
• Running routine audit checks.
• Screening transactions or payments for anomalies.

IT:

• Resetting employee passwords and unlocking accounts.
• Provisioning/deprovisioning user access when employees join or leave.
• Monitoring system logs for known error codes or suspicious activity.
• Running regular software patching or update checks.
• Resolving common helpdesk tickets (e.g., VPN setup, printer access).

Step 2: Choose a low-risk, high-value use case

Just because you’re starting small doesn’t mean your AI pilot has to be low-impact. When choosing your first pilot project, use the Impact-Effort matrix to identify projects that will deliver meaningful results if successful, but will do little damage if they fail.

Make sure your initial pilot programs meet the following criteria:

• High-volume process
• Low reputational risk
• Clear, measurable outcome (time saved, error reduction)

For example, automating payroll calculations to cut down on manual mistakes, using AI to screen resumes for baseline qualifications to speed up hiring, or applying compliance monitoring tools to flag missing documentation across jurisdictions.

As well as being low-risk/high-value, choose a pilot program that delivers results in weeks rather than months. Early wins build credibility, trust, and enthusiasm for AI, which helps to foster a culture of innovation. This makes it significantly easier to green-light more ambitious AI projects in the future.

If you’re designing your pilot program for HR, draw the line between what can be automated and what must still be done by humans. Make sure any pilots are on the right side of that line.

Step 3: Select the right AI tool or vendor

Once you’ve decided what you want to achieve, the next hurdle is selecting the right AI solution. The market is flooded with tools of all shapes and sizes, ranging from niche solutions for single workflows to comprehensive platforms that promise end-to-end automation.

To deepdive into this topic, check out our guide: Agents vs. Chatbots vs. Copilots: How to Choose the Best AI Tool. We go through different types of AI technologies, help you understand the capabilities and limitations of each, and give you a checklist for deciding which tool is right for the job.

When choosing the right AI vendor, it’s essential to prioritize credibility over hype. It’s easy for AI enthusiasts to reach for the tool that ‘can do everything.’

Here are some key things to look out for as you’re shopping for solutions:

• Track record in similar industries
• Transparency in data handling
• Integration with existing systems
• Scalability for future expansion
• Reliable and responsive customer service
• Examples of customer success stories
• Data security & compliance certifications (GDPR, SOC 2)

Once you’ve narrowed down your search to the top contenders, you may need extra information to make an informed decision. This is especially important if you’re using AI within HR and payroll, as accuracy, data privacy, and compliance are on the line.

If possible, book a demo of your chosen tools to get a better understanding and ask more specific questions, such as:

• How do you prevent bias/errors?
• How do you secure sensitive employee data?
• What’s the average time to ROI?

Choosing the right tool for a pilot program means staying focused on one main use case and success metric, and keeping the costs relatively low. That means not jumping in at the deep end with a complex, comprehensive tool that promises to do everything. However, keep the future in mind. If the pilot is successful, you’ll need a tool that can scale with you. In practice, that means choosing tools your non-technical teams can actually use—and that won’t hit you with unreasonable costs once you move past the starter tier.

Step 4: Measure results and iterate

Another key to ensuring success with AI is to properly measure ROI. For a pilot program, it’s better to choose one success metric to chase, or at least to set one metric as primary. This could be time reduction, cost savings, risk mitigation, or various others. For more on this topic, check out our complete guide on how to measure the ROI of AI.

But a successful pilot project is shown through more than just ROI. Before iterating, collect qualitative user feedback from the teams involved. Focus on different aspects of the pilot, such as:

• Usability and adoption: “How easy was the tool to use?” “Did it integrate smoothly into workflows?”
• Accuracy and reliability: “How often do users need to step in and correct errors?” “Are the errors minor inconveniences or major blockers?”
• Efficiency and value: “How much time is being saved compared to before?” “Has it reduced repetitive tasks or manual effort?”
• Work experience: “Has the pilot improved job satisfaction or reduced frustration?” “Do users feel more confident in their work with AI support?”
• Next steps: “Which related tasks do employees wish could be automated next?” “Are there blind spots or risks they’ve noticed that weren’t part of the initial scope?”

Once your pilot has proven value, stabilized performance, and earned user trust, you can begin iteration planning. Good iteration means improving performance and growing in scope, without losing focus.

Expand scope gradually

Start with a single process or team, then roll out to additional teams, regions, or workflows once results are validated. If successful, your new AI tool or process can be rolled out company-wide, but resist the urge to launch prematurely. Balance the speed of expansion with maintaining control and oversight, using early users as internal champions to build excitement and boost adoption.

Increase complexity one step at a time

If you started with simple, rules-based tasks, move on to more moderately complex tasks. For example, going from simple data entry to documentation verification. Follow the same steps and best practices as with your original pilot. Cutting no corners and keeping humans in the loop for oversight is key to ensuring compliance and lowering risk as complexity increases.

Integrate with existing systems

If you’ve kept your pilot isolated to a sandbox environment, you can now begin integration. In the second iteration, connect your pilot to HRIS, payroll platforms, or compliance dashboards so it runs within the systems employees already use. Automate data flows so pilots feel like part of the existing tech stack, and provide training so users know how to access the AI-enabled features. This boosts adoption and reduces friction, ultimately leading to better results.

Tighten feedback loops

Run feedback cycles - automating them if possible to avoid them slipping through the cracks. In your surveys, gather both structured (ratings, checklists) and unstructured (comments, interviews) feedback.

It’s critical to prioritize feedback from frontline users, not just managers. They experience the tool day-to-day and can provide the most accurate insights on usability, efficiency, and time saved. Transparency is essential for frictionless AI adoption, so show employees how their input shapes improvements and emphasize that raising issues early helps everyone succeed.

Avoiding common AI pilot pitfalls

Going back to the research we started with, we know that only 5% of AI pilot programs deliver meaningful business results. So what are the common pitfalls keeping this number low? Aside from not following the best practices we’ve listed here, there are few more to consider:

Not seeking expert knowledge

According to research by MIT, AI pilots developed with external partnerships have nearly double the rate of success compared with internal builds (about 67% versus 33%). Internal teams know the business inside and out, but are they the top experts in building AI processes and products? It’s easy to think pilots should be DIY, but with AI technologies evolving so quickly, that approach may backfire.

As long as your pilot is well balanced on the Impact-Effort matrix, pursuing an external partnership is worth the investment. The right vendor brings expertise, infrastructure, and guardrails that speed up results and reduce risk.

Ignoring integration

Asking busy teams to log in and out of new platforms causes too much friction in adoption. Not only that, but fragmented systems lead to potential points of failure. While it’s fine to test pilots in a sandbox (and is encouraged when the pilot handles sensitive data), successful programs should ultimately integrate into the systems employees already use.

Underestimating the importance of change readiness

A change-ready culture doesn’t happen by itself. It needs to be fostered. This is especially true when the change is AI, something which can be a cause for anxiety. Change readiness can be encouraged by framing AI pilots as enablers, not replacements, and through clear communication about how this benefits workers and not just leadership. Training in AI literacy reinforces this, and also flattens the learning curve – both of which assist adoption.

AI that builds confidence

A great way to see how AI can enhance your teams - without having to worry about a steep learning curve or compliance risks - is Deel AI Workforce.

With Deel AI Workforce, you get a suite of AI agents ready to join your team and proactively pick up the repetitive tasks that keep your people from doing great work. They’re easy to use, whether you choose our pre-made agents or build your own. And with our advanced enterprise-grade encryption, your data is in safe hands.

Sign up today, and get ready to welcome Deel AI Workforce to your team.