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1Build a business case for integrating AI
To justify the time and expense of implementing AI , you should build a case for how it will ultimately increase profits or reduce expenses . You should identify at least three value drivers , such as decreasing headcount , increasing yield , and reducing scrap . Then , build a calculation of the expected savings starting from the metrics you ’ ll be able to move with the technology . This forms the basis of a return on investment ( ROI ) business case to justify the expense of implementing a particular technology .
2
Get the data ( in the cloud )
It ’ s not uncommon for useful manufacturing datasets to be siloed , but AI has to be trained on a dataset — images , videos , functional test results , or any metric important to your value drivers . Data , therefore , needs to be centralized in a structured way that ’ s easily accessible . You might believe you should do this step yourself first , but Anna advises working with a partner who will not only aggregate this data with a drop-in infrastructure but will be responsible for getting value out of it . An internal DIY effort rarely bears fruit because that second part is missing . Aggregating and organizing your data is crucial for accessing AI ’ s most powerful insights .
However you aggregate that data , Anna argues , it should be in the cloud . This enables data aggregation across many physical locations , remote administration and oversight , remote access for the engineers using the data , and the highest likelihood it will be futureproofed to leverage further advancements . Even regulated manufacturers are leveraging cloud data for these reasons .
3Select AI technologies that easily provide value
Leverage your business case to select AI technology that will clearly move the needle on your value drivers . Some technologies require you to have your own AI engineers or data scientists to really make use of them . Unless you already have these people on your payroll , don ’ t buy a tool that requires them . Choose an AI use case that will be easy to train and can leverage your team ’ s existing expertise .
4
Establish
Proof of Value
Design your implementation of the AI technology as a Proof of Value ( POV ), not a Proof of Concept . A Proof of Concept shows that the technology can do what it ’ s supposed to ( like collecting the metrics it was instructed to collect ), but Proof of Value focuses on how this technology influences the value drivers in your business case . If your chosen technology isn ’ t increasing yields or reducing scrap as expected ( and at this point , you ’ ll have the concrete data you need ), you ’ ll know before you make a large investment .
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