Four best practices for incorporating Artificial Intelligence and Machine Learning into your operations
The challenge for industries that may be new to this technology is that the demand for AI and ML applications is growing so rapidly that technology vendors tend to provide generic methodologies and tools, and this approach may be missing some key parts for the overall solution to truly be effective. The advantage of being a late adopter is the opportunity to learn best practices from other industries. This article presents best practices for organizations that want to integrate AI and ML into their processes.
Bridging the language divide
Often, AI and ML providers speak a different language than organizations. Good communication is key to enabling new AI and ML solutions and will minimize disruptions through the integration process, manage expectations for short and long-term gains, and anticipate the overall return on investment. That’s why it’s important, right at the start, to bridge the two and translate an operator’s specific requirements in order to truly understand what type of optimization has been done in the past and the problem we are really trying to solve. Not all optimization approaches are created equal and there’s a certain optimal sequence to follow—you wouldn’t want to eat your dessert before your main course, after all. In my field, we often discover that the initial problem needs to be reframed. With good dialogue, data scientists can conceptualize the right path to custom built tools and solutions that will solve the operator’s specific problems the first time around.
Measuring the right data
One of the easiest places to go wrong is thinking that the more data you have, the more powerful your AI and ML solution will be. The reality is, not all the data your operation generates will be useful for an AI and ML solution, and not all data that you need is being collected! Companies need to ensure that the right data is properly measured and stored in the right way. They need to know exactly how and where to collect it and understand how it will be used. Communication again will be key to establishing the right data strategy in support of the problem you have defined. By starting on the right path, you will successfully set up, organize, and manage a coherent and prosperous data strategy.
Developing actionable insights
Data in systems can become so tangled that you can’t interpret it anymore. Once data scientists can stabilize the data, they can establish the appropriate relationships between the data and/or diagnose where the bottlenecks are. However, it can still be a challenge to really know where to start and how to address the problems. To overcome these barriers, operators need to understand the capabilities of data-driven solutions and data scientists need to have comprehensive knowledge about the sector and how it operates. Having data scientists on the team that understand your sector intimately is your secret weapon and can make all the difference between a fruitless project and a successful one.
Overcoming cultural hurdles and building trust
One of the biggest hurdles in AI and ML adoption is having realistic expectations and trusting the capability of the technology and the reputation of the partners you are working with. This is particularly true in high-risk environments where even small changes can have big financial or safety impacts.
Essentially, you need a well-developed plan to transition from existing processes to more advanced solutions. This plan can guide companies through their unique paths to AI and ML adoption, developing a roadmap, vetting specific tools and providers in advance.
There is no doubt that the potential for AI and ML to positively impact operations is considerable. Ultimately, a constructive collaboration between operators and data scientists with sector knowledge is key to incorporating AI and ML best practices into your organization successfully.