AI Pilot vs. POC: What’s the Difference?

Key Takeaways

• POCs are small, controlled tests that validate whether an AI concept is feasible.
• Pilot projects simulate production environments to test scalability and performance.
• POCs focus on core functionalities with limited scope, while pilots involve more resources and engage end-users for comprehensive evaluation.
• The choice between a POC and pilot projects depends on company goals, risk tolerance, and available resources.
• Both are crucial steps in AI development, each offering valuable insights at different stages.
• Multimodal offers both paid POCs and pilots to provide tailored risk-mitigated solutions.

What Comes First, Pilot Projects or Proof of Concept (POC)?

When venturing into technology projects, including AI projects, businesses often wonder whether to start with a proof of concept (POC) or a pilot. The general rule is that a POC comes first. 

It’s a smaller, more controlled test to prove whether a technology or idea can work. Once that’s validated, companies move on to a pilot, which is closer to a real-world implementation.

Both POCs and pilots play a crucial role in reducing risk and ensuring that AI solutions meet business needs.

• For example, at Multimodal, we frequently begin with paid POCs for enterprises that need to test new AI concepts with a small experimental budget. 
• In mid-market businesses, we often skip directly to pilots since these companies are typically ready for more extensive concept testing right away.

Keep in mind that if someone offers a free pilot, it’s likely just a live demo, not a true pilot. You need to consider what you're trying to validate—is it the solution’s feasibility (POC) or the provider’s ability to deliver it at scale (pilot)? 

Proper POC and pilot implementations should be customized to your company’s needs, which requires a paid approach to ensure the right level of commitment and tailored solutions. It also mitigates financial and operational risks for both parties.

That’s why we offer both paid POCs and pilots on a smaller, controlled scale before committing to a full-scale implementation. This way, we can provide real value with tangible results and help you make informed decisions by aligning your expectations and building trust. 

Ultimately, the starting point depends on your company goals, available resources, and risk tolerance. 

But first, let’s explain in more detail what POCs and pilots are and how we approach both processes.

What is Proof of Concept (POC)?

Before implementing AI in business, decision-makers need to ask themselves many questions. The crucial ones are whether AI is the best next step for their company and how the technology will fit their needs. 

A Proof of Concept (POC) is an early-stage project that tests whether a particular AI concept or technology is feasible. The goal is to determine whether the idea can be successfully implemented in a specific environment. 

POC in AI aims to answer other important questions like:

• Will this AI technology solve our specific business problems?
• Does the solution perform as promised?
• Will our teams adapt to and benefit from this AI-driven approach?
• Is the final AI implementation technically and operationally feasible?

How Does a Proof of Concept Work

1. Feasibility demonstration: A POC aims to show that a particular idea or AI technology is feasible and works as intended within a specific context.
2. Scope and scale: The POC typically focuses on core functionalities rather than a complete solution, keeping the project small and manageable.
3. Objective: The primary goal is to validate the concept before investing further. It helps ensure that the AI solution can solve real-world problems and meet business objectives.
4. Risk mitigation: A POC helps reduce uncertainties in the development process by identifying potential risks and challenges early on.
5. Feedback and iteration: During the POC, stakeholders—including users, investors, and team members—provide feedback to refine the concept and address issues. Strong POC results will help secure buy-in from stakeholders moving forward.
6. Documentation and reporting: The POC results provide documented evidence of feasibility, helping guide decisions on developing future solutions and attracting investment.

For instance, in one of our successful case studies, we ran a POC for Caktus, deploying a Modal transformer library to test fine-tuning large language models (LLMs) for essay generation. This small-scale experiment allowed us to demonstrate the model’s capabilities and refine the concept based on assessment before moving on to a pilot.

To summarize, a POC is a key step that validates business ideas’ practicality and viability. It enables stakeholders to make informed decisions on investment and development.

What is a Pilot Project?

One way to think of a pilot project is “one step away from full production.” It is the next stage and a more advanced, real-world test of an AI solution. It simulates a production environment, engaging more users and testing the solution’s performance under real conditions.

Essential Features of a Pilot

1. Scope: A pilot has a broader scope than our POC, involving more features and capabilities of the AI solution.
2. Duration: Pilots usually last for about three months to allow for a more comprehensive evaluation.
3. Objectives: Our main goals are:some text
   • Test the full solution in a real-world environment.
   • Evaluate its performance, scalability, and user experience.
   • Identify potential issues and resolve them before full deployment.
   • Demonstrate value and return on investment (ROI) and also monitor pre-established metrics.
4. Risk mitigation: Pilots help create a real-world environment where prospects can explore our AI technology at a low risk before committing to a full implementation.
5. User involvement: The pilot engages users across the organization to gather feedback and facilitate change management.
6. Data collection: Usage metrics and performance data are collected during the pilot to measure success and prove the solution’s value.
7. Evaluation: After the pilot, results are thoroughly analyzed to decide whether the solution meets the customer’s expectations and goals.
8. Resources: Pilots require more resources and investment than a POC, often closely resembling a full implementation.
9. Transition potential: Pilots are designed to transition smoothly into production because they meticulously evaluate the solution's performance in the customer’s environment. This makes them a critical step before full-scale deployment.

In another example, we conducted a pilot for Talent after a successful POC. In the POC, we tested resume analysis using LLMs. The pilot expanded on that concept by deploying a Docker Container (API) for clients to access and evaluate scalability. The pilot provided valuable insights and laid the foundation for full implementation.

What is the Difference Between a POC and a Pilot Project?

In AI development, the distinction between a proof of concept vs. pilot is essential. Both have specific purposes and offer different levels of risk, resource commitment, and results. However, the main difference between them lies in their scope and objectives.

While a POC is about proof and testing in a controlled environment, a pilot simulates full production, often with a larger scope and more users involved.

• Scope: A POC has a narrow focus, designed to test critical aspects of a solution, while a pilot involves a larger, more comprehensive implementation.
• Duration: POCs are short-term and focus on feasibility, while pilots last longer (usually several months) to test the solution in real-world conditions.
• Risk: POCs involve less risk since they require fewer resources, while pilots demand more investment but provide a deeper evaluation of the solution.
• Outcome: A POC helps decide whether an idea is worth pursuing, while a pilot determines whether the solution is ready for full-scale deployment.

When Should You Use Pilot vs. POC?

Choosing between pilot projects and POCs depends on the project's objectives. 

• If your goal is to test how a solution works in a real-world setting and gather feedback, opt for a pilot. 
• On the other hand, if you need to validate whether a concept is feasible and worth pursuing, a POC is a better option.

Pilot projects help collect data and user feedback from stakeholders and identify areas for improvement, while POCs focus on proving an idea’s viability and identifying any early obstacles. 

Ultimately, both are essential steps in AI development, offering different levels of validation and insight. But the choice between them depends on your company's stage and goals.

Are You Ready To Elevate Your Business With Artificial Intelligence?

We believe that starting with a POC or pilot project is essential for mitigating risks and aligning expectations between our team and our clients. Our structured approach ensures our clients can make informed decisions while minimizing financial and operational risks.

By offering a paid POC and pilot project, we demonstrate confidence in our product's efficacy while providing clients with tangible results that justify the investment.

If you are still not ready to take these steps with us but are interested in seeing our AI models in action, schedule a free 30-minute call with our team, and let’s discuss your specific needs.

FAQs

What Is the Difference Between POC and MVP?

A POC tests feasibility, while a Minimum Viable Product (MVP) is a functional product with enough features to attract early adopters. POCs validate concepts, while MVPs are closer to a fully usable product.

What Is the Difference Between a Prototype and a POC?

A prototype is a working model used to demonstrate how a product works, often focused on design. A proof of concept, in contrast, focuses on technical feasibility, proving whether an idea can be implemented at all.