Manufacturing companies need information architecture before artificial intelligence

Manufacturing companies need information architecture before artificial intelligence

By Anand Mahurkar, CEO of Findability Sciences

The growth of the manufacturing industry depends on the continuous search for new ways to increase revenue, reduce risk and error, and improve production efficiency. Machines of AI-based solutions are the perfect way to achieve these goals, as they allow companies to leverage their vast legacy of manufacturing data to automate complex tasks, self-optimize, and initiate decision making. independent decision.

AI in the manufacturing market is valued at $2.3 billion in 2022 and is expected to reach $16.3 billion by 2027. Post-pandemic, demand for AI is growing, and not just in manufacturing . Many businesses now see the need for AI to keep pace with today’s business landscape. According According to a recent PwC report, AI technology is expected to contribute up to $15.7 trillion to the global economy by 2030 and will continue to be a game changer by accelerating productivity.

AI in manufacturing can enable highly accurate forecasting, resource requirement forecasting, and energy and raw material price forecasting. But barriers to intelligently implementing AI strategies persist.

Consider this aspect: AI requires machine learning, machine learning requires analytics, and analytics requires the right data and information architecture (or AI). Simply put, there is no AI without AI.

AI solution providers should aim to help organizations become data-driven so that they can take full advantage of AI and be able to use their data to generate insights and predictions. But for the organization to be successful on its AI journey, it needs to prepare its system for AI innovation. This means working on their AI before the AI.

Failing to prioritize information architecture can cause the AI ​​journey to end like the 60-80% of AI projects…fail. Many manufacturers are loaded with data but lack the right infrastructure to interpret it. Data must be collected, cleaned and analyzed in order to be properly fed by the algorithms; as you probably know, data collection and storage is not always handled properly.

There is also this complicated problem: many companies have invested in many diverse technologies but do not know how to use these technologies to create an AI program. For example, a company may have obtained licenses from big data companies such as IBM or Snowflake, but do not know how to use them to create a sustainable AI program.

The vast majority of industrial organizations find data quality to be a drag on their data integration projects. Most do not analyze their data. Those that do spend too much time cleaning, integrating, and preparing data. And most companies are hampered by data silos, i.e. data from different departments that is not shared with the entire organization.

In search of AI solutions

The right AI solution provider can help a manufacturing company on its AI journey by:

Creation of a collaborative team: The AI ​​solution provider can partner with employees of the organization to create a network of experts. The solution provider can teach and train employees on how to manage the AI ​​program. Different experts can provide different information to help the company achieve its goals. This collaborative team can help create a roadmap for enterprise AI solutions.

Organization of an information architecture: manufacturing companies can get ahead by using “big data”, i.e. data with a healthy variety in terms of source, type and even format. Both structured and unstructured data constitute “extended data”. Collecting huge volumes of data (big data) is not enough for a journey to AI to be successful. AI solution providers can help the organization create an information architecture so that it can leverage both big data and big data.

Implementing a partnership strategy and migrating to the cloud: A trend in the technology space is to move from on-premises to cloud. On-premises is a way to deploy technology via hardware such as flash drives or CDs. Companies have spent a fortune on technologies such as an Oracle database or an SQL database. But with cloud technology, software and licenses have become more accessible across multiple devices. Platforms such as Snowflake, EDB, Amazon Redshift, DB2, Netezza, and IIAS (IBM Integrated Analytics Systems) can contribute to a seamless AI journey by moving both data and system to the cloud.

Since they have partnerships with these platforms, AI solution providers can bring all the technology, skills, and expertise needed to help the business migrate to modern cloud-based data storage solutions. They know how to connect all the technologies necessary for a flawless AI implementation, from planning to execution.


Overall, the AI ​​journey for manufacturing is still fraught with challenges. Manufacturing companies are still struggling with data silos as well as misguided investments in technologies that are not AI-ready.

Manufacturing companies will benefit greatly from AI, but they need to take several steps to start their AI journey. Seeing the big picture – all the benefits that AI technology can bring to industry – should be a powerful motivator for manufacturing companies to accelerate their digital transformations.

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