Scoping a pilot project for AI experimentation is a crucial step in ensuring its success and paving the way for broader implementation. This week’s focus is on how to effectively define the scope of your pilot project, starting with small, manageable areas such as specific departments or processes. By clearly outlining what is included and excluded, setting realistic deliverables, and identifying necessary resources and potential risks, you can create a solid foundation for your AI initiative, ultimately driving early wins and long-term success.
Determining the Scope of Your Pilot Project
Last week I spoke about determining which goals are most important for your organization and how to ensure that the goals are achievable. This week I am talking about determining the scope of your pilot project. Because your pilot project is a unique opportunity to get an early win and pave the path of success for you and your project, getting the scope right remains critical.
Determine Pilot Scope
Determining the pilot scope is essential. It’s always easier to start small, of course. Starting small also makes it easier to get the requisite approvals. Do you want to focus on a particular department or process, just to start? You could start with Manufacturing and Production – perhaps Quality Control or Inspection. You could start with Supply Chain and Inventory Management – Demand Forecasting or Logistics Optimization might be a good process to start with. Customer Service and Experience is a popular place to start because it’s relatively easy to implement a Chatbot or Personalized Marketing through AI. In the areas of Sales and Dealership Operations, you might choose lead scoring and management or price optimization. Of course, these are just a few ideas for experimentation.
Example: AI for Predictive Maintenance in Connected Vehicles
Let’s say, for example, that you wanted to use AI for Predictive Maintenance in the Connected Vehicle. For the scope, you’d want to determine what to include – and also what to NOT include. You might want to include data from vehicle sensors, telematics, and service records, machine learning models to predict component failures and maintenance needs, pilot testing on a fleet of connected vehicles, and necessary integrations with existing vehicle maintenance systems. Likewise, you’d also want to determine what is OUT of scope for a pilot project. You might decide the following are OUT of scope for a pilot project: non-connected vehicles and non-critical components, like interior fittings.
Finalizing the Scope
For the scope to be finalized, you’d probably want to agree upon the deliverables. They might include a predictive maintenance algorithm to test (or several), pilot test reports and validation results performed by an expert in the field, a deployment strategy, and technical documentation.
Required Resources
What resources would you need to shore up to determine what can be “in scope?” You would probably want to nail down the data you’d need. Perhaps you’d say that you need the data from 500 connected vehicles over the past 3 years. You’d probably need an AI platform, specifically tailored for the automotive industry. (That’s where Humaxa comes in!) You would need help in the form of automotive engineers, data scientists with automotive experience, and perhaps a project manager. Of course, we have those types of human resources on our team here at Humaxa and we’re happy to help if needed.
Setting a Timeline and Budget
Of course, you’d want to set a timeline and a budget for the scope of the pilot project. When you establish your scope, you’ll also want to establish what risks are associated within the scope of your project and how you will mitigate such risks. For example, what risks might be associated with data quality? You might be able to mitigate that risk by running a rigorous data validation process. What about regulatory compliance? As most people know, automotive industry regulations – especially those around safety and security – change often. To mitigate that risk, you could hire a regulatory expert (or just use Humaxa’s AI to ensure compliance.)
Achieving Preliminary Successes
Lastly, you will probably want to make sure you can attain some preliminary successes, just within the scope of your proof of concept. Let’s say that you’re aiming for 95% accuracy in failure predictions, reducing maintenance costs by 20%, and decreasing vehicle downtime by 25%. Will your proposed scope allow you to demonstrate these successes, even within the limitations of a proof of concept?
Good luck to you and please let us know if you’d like some help.
