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.
Collaboration for Trust in Data – Black Box/White Box Balance
This issue of confidence is important in ML for business. Executives and operations teams often demand to understand the recommendations and algorithms used to produce results, but this too often forces data scientists to sacrifice model efficacy in order to boost legibility. A better option is to allow specialists to craft optimal algorithms and to share their work and data with peers, and to ”whitebox” only the context for non-specialists, providing visibility and drill down into the parameters and data used, but not the algorithms themselves.
Collaboration to Scale the Benefits to the Broader Organization
Likewise, it is important to industrialize ML outputs that prove to be of value. This means packaging and integrating them in a platform to make them accessible to all enterprise applications, across all teams and lifecycle processes. Using a platform strategy to break down analytical silos also enables critical governance for ML products, supporting standardization, certification, IP protection, personnel training (a perennial challenge), and traceability.
Without such governance and industrialization, ML cannot truly support continuous process or product innovation and improvement.
But with the right discipline and technology choices, these challenges are all addressable. What’s most important now is to define the right digital business platform for your business in order to enable the right collaboration capabilities across your organization, and set your company on the path to leveraging AI and ML for your digital transformation. An evaluation should be performed to identify the internal capabilities and talents required in each of these collaboration areas, and a blueprint developed for leveraging these internal and external resources. With competition tight for data science professionals, a more efficient collaboration will be mandatory.
In the next blog installment, we will explore how to realize the full value of Artificial Intelligence in industrial contexts.
Part 5: Key Challenges of Artificial Intelligence in Industrial Sectors
Read our White Paper on Artificial Intelligence In Industrial Markets
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