This is Part 5 in a blog series about industrial uses for Artificial Intelligence (AI). Previously we explored the benefits promised by AI. Now, let’s look at the issues that industries face as they try to integrate AI into their business processes and how to overcome those challenges.
To reap the full value of quality improvements and other potential AI and machine learning (ML) benefits, several important challenges will need to be overcome. Scaling AI and ML at the scale of the organization requires more than just the right amount of data and the right infrastructure. Scaling ML requires collaboration.
Collaboration vs. Data Silos – In-Context Information is Still Stuck in Silos
One of the core challenges of ML is providing it with the proper fuel, that is to say the right data. Getting the right data usually requires tackling enterprise silos, including data silos and silos of analysts and analytical products.
On the data side, this means combining disparate types of internal and external data (sound, images, text, 3D files, structured data, etc.) in order to provide the full context for design, production or field operations issues. This context is valuable not only because it enables smarter, better-informed analyses and recommendations, but because it gives human beings greater confidence in relying on machine-generated answers.
Part 2: Industrial applications of Artificial Intelligence and Machine Learning
Part 3: Differentiating Between Artificial Intelligence, Machine Learning and Deep Learning
Part 4: Benefits of Machine Learning in Industrial Contexts
Part 5: Key Challenges of Artificial Intelligence in Industrial Sectors
Part 6: Realizing the Value of Artificial Intelligence in Industrial Sectors
Part 7: Artificial Intelligence and Machine Learning at Dassault Systèmes
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