Executive Corner: Generative AI-Infused Advanced Analytics Fuels Digital Transformation

Despite spending billions of dollars on digital transformation, nearly 90% of companies are still not achieving their desired results, according to the ARC Advisory Group’s December 2022 Survey of Manufacturers. These shortfalls can be attributed to unused or underutilized data, in addition to adopting technologies that are often too complex for subject matter experts to leverage fully.

For these reasons, generative artificial intelligence (GenAI) emerged over the last two years at the perfect time. This transformative technology, a type of artificial intelligence capable of generating new content—such as text, code and images—in response to user prompts, has the potential to reshape the way industrial organizations analyze data, optimize operations and make critical decisions. However, the journey from raw data to meaningful insight is still disjointed for many organizations, making it difficult to harness the power of GenAI to uncover more meaningful insights.

As a result, there is a fervent need for software that empowers engineering, operations and management personnel to achieve faster and more valuable insights from their data, and to act on these insights to achieve measurable business impact. To this end, industrial organizations are achieving success by incorporating GenAI within advanced analytics software, enabling domain experts to harness the software’s power while increasing its effectiveness.

GenAI to improve efficiency

GenAI large language models understand human input and efficiently produce text and computer code, while advanced analytics and monitoring software provides clear access to cleansed and contextualized time series and event data. By combining these two technologies, organizations can significantly bolster the power and capabilities of software solutions to recognize patterns, gather insights, make predictions and recommend actions.

To achieve the greatest success with this combination, the key ingredients—reliable enterprise data, advanced analytics and generative AI—must be combined in a workflow with domain experts at the core, not in the background. In fact, the most important technological consideration is how it enables people to adopt new practices and behaviors, in pursuit of specific areas of business value improvement.

Companies can experience immediate business impact by enriching their advanced analytics and process monitoring with GenAI, empowering employees to enhance their decision making and improving analytics efficiency across three key areas:

• Operational excellence. By providing summaries and detailed explanations in natural language, it is easier for domain experts to understand the full process picture and make data-driven decisions with better accuracy. The result is an ability to analyze massive datasets to identify trends, anomalies and opportunities, and enable proactive decision-making, leading to immediate operational improvements in production, quality and yield.

• Sustainability. Teams can achieve sustainability timelines with faster results and eliminate stumbling blocks in data and personnel infrastructure. For example, GenAI helps cross-functional teams working with diverse data sets aggregate information for impactful sustainability metrics, reducing the time and effort required for emissions reduction and compliance reporting.

• Workforce empowerment. GenAI can be used to power conversational and interactive user interfaces, making it easier for learners to master the crafts within their specific domains. With continuous connectivity to current knowledgebases, GenAI-based training also retains its relevancy, enhancing training sustainment. By providing streamlined access to modern technologies that make domain experts’ jobs easier, organizations can attract new talent and retain subject matter expertise by providing a platform that enables motivating impacts.

For a national energy company, infusing GenAI into its existing analytics platform led to measurable and significant time savings. The company’s instrumentation and controls team leveraged the Seeq AI Assistant, a GenAI resource embedded across the Seeq industrial analytics platform, to examine a complex relationship between temperature measurements and test-well insights. This optimization effort equated to millions of dollars of impact.

Analyzing this complex system requires pre-analysis with special data science techniques, a task that previously required more than four days to complete with support from an outside coding team. With the AI Assistant, however, this step now takes just 15 minutes, providing significantly more time to focus on the process analysis.
 

Limitations and risks

GenAI promises the potential for significant improvements in the future, but like integrating any new technology, organizations must also acknowledge its limitations and associated risks, including data challenges, a lack of transparency and data privacy concerns.

To begin with, GenAI results must be validated. The technology is only as good as the data and models it is trained with, and as the saying goes, garbage in equals garbage out.