High Fidelity
High Fidelity
High Fidelity
Behind-the-Scenes Simulation
Overview
A Behind-the-Scenes Simulation is a low-fidelity, high-evidence validation method that allows us to simulate the whole experience of a digital or automated product with a human manually delivering the service behind the scenes. Unlike the Service Simulation experiment, where customers know a person is providing the service, in the Behind-the-Scenes Simulation, the human involvement is hidden from the customer. At Future Foundry, we use this method to test complex digital services, AI-driven automation, or operationally heavy business models before committing to expensive technology builds. By manually handling requests, we gather real-world insights on workflow efficiency, customer expectations, and willingness to pay without writing a single line of code. This experiment is particularly useful when testing AI-powered solutions, automated customer service, or logistics-driven businesses, where early-stage automation could be costly and premature. By first running things manually, we determine where automation is actually needed before building the infrastructure.
A Behind-the-Scenes Simulation is a low-fidelity, high-evidence validation method that allows us to simulate the whole experience of a digital or automated product with a human manually delivering the service behind the scenes. Unlike the Service Simulation experiment, where customers know a person is providing the service, in the Behind-the-Scenes Simulation, the human involvement is hidden from the customer. At Future Foundry, we use this method to test complex digital services, AI-driven automation, or operationally heavy business models before committing to expensive technology builds. By manually handling requests, we gather real-world insights on workflow efficiency, customer expectations, and willingness to pay without writing a single line of code. This experiment is particularly useful when testing AI-powered solutions, automated customer service, or logistics-driven businesses, where early-stage automation could be costly and premature. By first running things manually, we determine where automation is actually needed before building the infrastructure.
A Behind-the-Scenes Simulation is a low-fidelity, high-evidence validation method that allows us to simulate the whole experience of a digital or automated product with a human manually delivering the service behind the scenes. Unlike the Service Simulation experiment, where customers know a person is providing the service, in the Behind-the-Scenes Simulation, the human involvement is hidden from the customer. At Future Foundry, we use this method to test complex digital services, AI-driven automation, or operationally heavy business models before committing to expensive technology builds. By manually handling requests, we gather real-world insights on workflow efficiency, customer expectations, and willingness to pay without writing a single line of code. This experiment is particularly useful when testing AI-powered solutions, automated customer service, or logistics-driven businesses, where early-stage automation could be costly and premature. By first running things manually, we determine where automation is actually needed before building the infrastructure.
Process
We start by mapping out the intended automated customer journey, breaking it down into manual steps that can be handled by a human behind a digital interface. This could be a chatbot powered by live agents, a recommendation engine simulated through manual research, or an AI-driven service manually operated by a team. A simple landing page or app UI is created where customers interact with what appears to be a seamless service. In reality, every request is processed manually by our team. Customers receive the full experience as if automation were in place while we measure response times, failure points, and operational challenges. Throughout the test, we document how long each task takes, what elements cause bottlenecks, and where customers expect instant responses. If manual execution is too slow or expensive, we determine where automation should be introduced first. If users struggle with specific steps, it signals areas for improvement in UX and service design. At the end of the test, we analyse customer satisfaction, process inefficiencies, and pricing sensitivity, ensuring we only automate the areas that drive value.
We start by mapping out the intended automated customer journey, breaking it down into manual steps that can be handled by a human behind a digital interface. This could be a chatbot powered by live agents, a recommendation engine simulated through manual research, or an AI-driven service manually operated by a team. A simple landing page or app UI is created where customers interact with what appears to be a seamless service. In reality, every request is processed manually by our team. Customers receive the full experience as if automation were in place while we measure response times, failure points, and operational challenges. Throughout the test, we document how long each task takes, what elements cause bottlenecks, and where customers expect instant responses. If manual execution is too slow or expensive, we determine where automation should be introduced first. If users struggle with specific steps, it signals areas for improvement in UX and service design. At the end of the test, we analyse customer satisfaction, process inefficiencies, and pricing sensitivity, ensuring we only automate the areas that drive value.
We start by mapping out the intended automated customer journey, breaking it down into manual steps that can be handled by a human behind a digital interface. This could be a chatbot powered by live agents, a recommendation engine simulated through manual research, or an AI-driven service manually operated by a team. A simple landing page or app UI is created where customers interact with what appears to be a seamless service. In reality, every request is processed manually by our team. Customers receive the full experience as if automation were in place while we measure response times, failure points, and operational challenges. Throughout the test, we document how long each task takes, what elements cause bottlenecks, and where customers expect instant responses. If manual execution is too slow or expensive, we determine where automation should be introduced first. If users struggle with specific steps, it signals areas for improvement in UX and service design. At the end of the test, we analyse customer satisfaction, process inefficiencies, and pricing sensitivity, ensuring we only automate the areas that drive value.
Requirements
This test requires a digital front-end (landing page, chatbot, or interactive form), a small team to execute manual requests, and a way to measure operational performance. The strongest validation signals come from customers believing they are interacting with an automated system and continuing to use the service.
This test requires a digital front-end (landing page, chatbot, or interactive form), a small team to execute manual requests, and a way to measure operational performance. The strongest validation signals come from customers believing they are interacting with an automated system and continuing to use the service.
This test requires a digital front-end (landing page, chatbot, or interactive form), a small team to execute manual requests, and a way to measure operational performance. The strongest validation signals come from customers believing they are interacting with an automated system and continuing to use the service.
Discover other experiments
Explore more real-world experiments that have helped teams validate ideas, reduce risk, and accelerate innovation.
