How to create an AI roadmap
Robotics and artificial intelligence (AI) share a deep, intrinsic connection, with robotics acting as the critical interface that brings AI's computational intelligence into the physical realm. This partnership enables machines to engage dynamically with their environments and execute tasks independently, effectively merging digital and physical worlds in innovative ways.
Robotic companies are experienced in AI deployment
Companies specializing in robotics know much about using AI in practice. They use their extensive experience to apply AI in industrial environments, making them key partners in developing a comprehensive AI strategy. This includes advanced uses of AI, like using computer vision for automatic quality checks.
Defining an AI roadmap
An AI roadmap is a strategy for guiding the implementation of AI technologies in a business, for reaching certain business goals. For a manufacturing company, this might involve using AI to predict product demand, implementing predictive maintenance, automating quality checks, and improving how things are made to boost product quality. However, it is important to know there are some potential challenges to watch out for when starting with AI. For example, your machines might lack the relevant sensors, the data might not be accessible, dust and pollution might affect vision systems and measurements could be inconsistent.
The need for an AI roadmap
The integration of AI technology into a business will undoubtely change workflows and roles. It is therefore crucial to approach this transition with care. A well-thought-out AI strategy will guide you through each step of the process. As the figure below illustrates, getting your team involved and kicking off with small trial projects are at the core of such a well thought-out AI roadmap. Initial AI experiments let you test your AI concepts, observe their performance in real settings at your site, and tweak them based on actual results and feedback from the team involved.
How to start with AI: Dive into experimenting
From what we have experienced, the key to implement AI is much about trying things out. You need to see how AI might work with your specific data and at your particular site. Every place is different, so it's important to approach this with an open mind, ready to learn and discover. We advise to begin by testing different AI tools and approaches to find what works best for your company, directing your efforts to where you have the largest pain and, presumably, the highest gain.
During this early stage in AI implementation, it can be helpful working with experienced robotics companies. Such partnerships will give you a lot of practical knowledge, and insight into AI and robotics, that effectively can solve your real-world problems.
AI adoption roadmap, by nLink.
