The building blocks of AI in industry
The role of artificial intelligence in industry is currently in its most preliminary stages with simple applications leveraging the most preliminary stages of AI. However, as the technology continues to develop, smarter, larger models will be applied across broader enterprise data sets and more complex applications. Only by extending AI functionality beyond single, individual tasks, it can help support the goal of a true digital enterprise, allowing it support human-lead efforts in a new and previously impossible way.
In a recent podcast, Dr. James Loach, head of research at Senseye Predictive Maintenance, explored the impact of AI across industry as it continues to expand and take on new roles. While Senseye has already deployed custom AI models with great success, it is by no means the limit of what AI can accomplish, either within the scope of predictive maintenance or industry as a whole.
Check out the full episode here or keep reading for a summary of that conversation.
Applying AI across enterprise data
The idea of applying AI across broad data sets, especially those found in an enterprise setting, has grown increasingly popular as AI itself has exploded in popularity, however, it isn’t always clear what this application will actually entail. The method Senseye uses to interpret meaning from time series data offers one potential method for extracting value from broader data sets and potentially revealing connections that would be nearly impossible to find otherwise.
This technique relies on embeddings, which are a method used to mathematically represent what information means, which then allows an LLM to quickly find similar groups of information. This method can be used to help answer questions by finding information, since a question and answer will have related embeddings, but it can also be used to help cluster relevant information across broad data sets which can then be analyzed by an LLM.
Since all the data available within a company, including everything from designs, to production logs to factory sensors represents a staggering amount of information, being able to quickly and effectively group that information by similarities, even ones that aren’t obvious, offers great value in achieving a digital enterprise. Using this method combined with an advanced AI model will allow for the ideal of a digital enterprise that can take huge amounts of data into account to quickly and autonomously develop actionable insights to be realized, turning underutilized enterprise data into a valuable resource.
The future of AI in industry
Bringing AI to the enterprise isn’t just a matter of bringing AI and data together, James explains, but also about building the right kinds of models. Senseye’s time series foundation model is one such example, a specially designed and trained model for a specific task. These type of task-specific models will be key in bringing AI to the immensely complex realm of enterprise data, requiring them to learn and think in ways conversational chatbots do not.
Starting with smaller, domain specific models also helps manage cost and complexity for early stage AI development, rather than attempting to build a large, expensive model that tries to do everything, companies can instead focus on deploying AI for specific tasks and smaller scopes. This can lead to early ‘wins’ that prove the value of the technology while simultaneously serving as a starting point to continue development for more complex systems.
Artificial intelligence has a lot to offer yet must also contend with a lot of challenges. Only by building a smart framework, where data and AI applications are carefully chosen to best support specific goals will the true value of AI in industry be realized. Senseye’s time series foundation model represents a perfect example of this and a blueprint for the first stages of true AI adoption across industry going forward. Through dedicated effort on specific goals, AI will be given the chance to shine in industry and support continued digital advancement in innovative and unique ways.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
