Generative AI offers new possibilities for advanced analytics

The emergence of generative artificial intelligence (GenAI) brings exciting new prospects for industrial process analysis. This transformative technology generates content such as new text, code and images based on user prompts, offering process manufacturers the possibility to transform the way they analyze data, optimize operations and make critical decisions. This innovative capability enables companies to obtain the information they need more quickly and to use the generated content to guide decisions and improve industrial processes. The introduction of GenAI thus brings a powerful tool to industry that is expected to drive improvements in productivity and quality, leading to greater business success.

Interest in generative AI comes from the confusion process manufacturers feel when faced with a "data-rich and information-poor" situation, especially with the advent of the Industrial Internet of Things (IIoT), the volume, complexity and accessibility of operational and device data continues to increase. However, this excess data presents huge opportunities if it can be managed effectively.

The emergence of artificial intelligence and machine learning technologies offers the potential to uncover more meaningful insights, but for many organizations, the journey from raw data to meaningful insights remains a long one.

As a result, team members—including engineering, operations, and management—need software that can quickly derive valuable insights from data. Integrating generative AI into advanced analytics software will have an impact on the process industry as it makes it easier for domain experts to harness the power of the software, thereby increasing its effectiveness. With this software, team members can get data analysis results faster and take corresponding actions to improve production and business metrics. This will help improve the speed and accuracy of your team’s decision-making, thereby promoting business growth and development.

Using Generative AI to Enhance Advanced Analytics

Generative AI large language models excel at understanding human input and generating text and computer code. Advanced analytics solutions provide efficient access to cleansed and contextualized time series data, providing clear analytical results. Combining these two technologies can significantly improve a software solution's ability to identify patterns, gather insights, make predictions and provide action recommendations.

For generative AI-enhanced advanced analytics solutions to be successful, domain experts need to be provided with key elements so that they can perform effectively in alignment with business and technology strategies Analyze and make effective decisions.

To achieve maximum success, the key elements—reliable enterprise data, advanced analytics, and generative AI—need to be integrated with domain experts at the core, not just integrated in the backend (see Figure 1).

By enriching advanced analytics with generative AI, companies may gain many benefits, including:

• Enhanced decision-making: By providing summaries and detailed descriptions in natural language, domain experts can more effectively Easily understand the entire process and make data-driven decisions more accurately. The result is the ability to analyze massive data sets to identify trends, anomalies and opportunities and enable proactive decision-making.
• Higher analysis efficiency: Can quickly move from short, text-based task descriptions to functional computer code that performs those tasks, often with minimal adjustments and corrections. This enables domain experts such as engineers and data scientists to focus on high-value activities, reducing time to insights.
• Greater predictive capabilities: Generative AI improves the ability of algorithm-based analytics to detect anomalies, inform predictive maintenance, and predict production data. It also provides additional functionality for pattern detection, particularly in datasets representing sensor data combined with operating instructions or logs.
• Simplify onboarding and training: Generative AI can be used to support conversational and interactive user interfaces, making it easier for learners to master the craft of their manufacturing fields. Generative AI-based training also retains its relevance through continuous connections to the current knowledge base, thereby enhancing training retention.

By providing streamlined access to modern technologies that make domain experts’ jobs easier, companies can not only redefine business operations but also cultivate an inspired, engaged, and capable digital organization .

Limitations and Risks of Generative AI

While generative AI promises significant improvements in the future, organizations must acknowledge its limitations and associated risks. These challenges include data challenges, lack of transparency and data privacy issues.

Generative AI models are typically trained using public data sets that represent common human knowledge, which is available on the Internet but lacks private knowledge. This can lead to some inaccurate results due to the difficulty in removing the inherent bias present in the training data. Training models using domain-specific private data is cumbersome and technically difficult.

Complex generative AI models often look like a black box from the front end with no interpretability, which makes explaining the decision-making process challenging. Persons using models must exercise caution. When these models feed data to other software, it adds a layer of complexity when filtering generative AI results to reduce the spread of disinformation, creating the risk of harm.

When deploying generative AI in sensitive industries, data privacy and security issues must be addressed. Because generative AI platforms are open to the internet for model training, developers and implementers must be careful to separate confidential information from public-facing components to avoid leaking data.

As media hype about generative AI continues to grow, companies should also be wary of common misconceptions. Despite popular discourse, generative AI requires human supervision to operate effectively. It does not replace the need for domain experts, but rather complements their expertise.

Building effective generative AI models requires a lot of time and effort. It’s not a panacea for instant solutions. When deployed in process industries, these models need to be fine-tuned and customized to meet specific needs. Off-the-shelf solutions may not produce optimal or even reasonable results.

Three Key Elements of Preparation and Implementation

To assess readiness to use generative AI to enhance process system data analysis, enterprises should examine three key attributes:

• Data Quality: Assess the completeness and accessibility of process data. High-quality data is critical to the effectiveness of generative AI and its relevance to the specific process problems being solved by the teams working on them.
• Skills Expertise: Assess proficiency in data science and AI relevant to process industries. Determine whether employees have the skills to develop and maintain generative AI solutions and understand the processes and business teams for which the solutions are intended.
• Infrastructure: Ensure the necessary computing infrastructure and data storage capabilities are in place to support resource-intensive generative AI deployments.

After considering these key factors above, enterprises should also follow the following guidelines to successfully apply and deploy generative AI:

• Invest in skills: in employees Conduct training in data science and AI while developing internal expertise to effectively drive generative AI initiatives.
• Define standards: Establish robust data governance practices to ensure data quality, privacy and compliance with industry regulations.
• Start Small: Start with pilot projects to test the applicability of generative AI to your organization’s specific use cases before scaling up.
• Promote continuous learning: Cultivate a culture that pursues knowledge and adapts as generative AI technologies evolve.

Unleashing the potential of generative AI

Generative AI has the potential to revolutionize industrial data analysis and decision-making methods. By combining generative AI with advanced analytics, process manufacturers can take efficiency, accuracy and innovation to new levels. Realizing the full potential of generative AI requires careful consideration of its limitations and risks, and a strategic approach to preparing your organization.

Process experts can leverage the power of generative AI to smartly integrate these solutions into workflows to drive favorable outcomes and stay ahead in an increasingly competitive environment.

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