But digital waste is the hidden inefficiency that’s just as costly. It includes:

𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚: Factories generate massive amounts of data, but much of it is never analyzed or leveraged for decision-making.

𝐈𝐧𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: Engineers waste time manually entering, cleaning, or searching for information that should be automated.

𝐒𝐢𝐥𝐨𝐞𝐝 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Key insights are trapped in different departments or legacy systems, preventing AI-driven optimization.

Digital waste silently drains resources, increasing operational costs while blocking AI from delivering its full potential.

Once manufacturers recognize digital waste, the next step is identifying where AI can generate the biggest returns.

To learn more about finding opportunities for the application of AI in manufacturing, I recently sat down with Patrick Byrne, Co-Founder and CEO of Annora AI.