Drive Productivity with GenAI-Infused Advanced Analytics

Drive Productivity with GenAI-Infused Advanced Analytics

Industrial organizations are reeling to meet ambitious objectives in a quickly-evolving manufacturing landscape. For example, while an increasing global focus on sustainability has prompted companies to integrate net-zero pledges into their visions and strategies, 93% will fall short without at least doubling their current pace of emissions reduction by or before 2030.

 

Data remains the key enabler for achieving ecological and other operational objectives. However, the ability to convert raw data to meaningful and actionable insights is still a long way off from reaching its full potential. In fact, nearly half—49%—of global business leaders cite data collection and reporting as a primary obstacle to meeting their net-zero, emissions reduction and overall optimization goals.

 

Fortunately, generative artificial intelligence (GenAI) is changing this narrative for innovative industrial organizations, providing the potential to reshape data analysis, operational optimization and critical decision-making. Additionally, the simultaneous surge in machine learning (ML) technologies is rapidly improving insight generation capabilities. Yet, the journey from data to insight is still disjointed for many companies.

 

These challenges are owed to a myriad of factors, but a primary reason is lacking sufficient software that enables teams to achieve and act on valuable insights from their data. By incorporating GenAI within advanced analytics platforms, industrial organizations can provide these capabilities to domain experts, empowering them to harness technology’s power of analysis while increasing operational effectiveness.

Like implementing any new technology, organizations will face challenges integrating GenAI and ML into their workflows. This column will highlight some of the leading obstacles and propose solutions, helping ensure organizations are prepared to leverage these tools effectively and achieve new levels of operational efficiency, production uptime and product innovation.

Infusing analytics with GenAI

GenAI large language models receive human input and process it to efficiently produce text and computer code, while advanced analytics platforms specialize in providing access to cleansed and contextualized time series and event data, as well as extracting insights from it. Integrating these technologies enables organizations to strengthen the power and capabilities of their overall software solutions to recognize patterns, gather deeper insights, make predictions and recommend actions.

 

Organizations can achieve the greatest success by combining reliable enterprise data with advanced analytics and GenAI. However, these three ingredients must be conjoined in workflows with domain experts at the core, not in the background (Figure 1).

 

Figure 1: Achieving success with GenAI-augmented advanced analytics solutions requires empowering domain experts with the key ingredients—reliable enterprise data, advanced analytics, and GenAI—to analyze efficiently and make effective decisions, in a union of business and technological strategies (courtesy of Seeq).



Companies can realize quick business impact by bolstering their advanced analytics platform monitoring with GenAI, enhancing analytics efficiency and empowering employees with information for better decision-making. When it comes to sustainability, this helps teams eliminate stumbling blocks related to data and personnel infrastructure, and achieve timelines with faster results. For example, GenAI assists cross-functional teams working with diverse data sets to aggregate information and develop sustainability use cases for impactful reporting.

 

Eliminating barriers in frontline and corporate teams reduces the time and effort required to create emissions reduction plans and compliance reports. These capabilities also simplify combining sensor datasets with operational notes and logs, creating enterprise-wide sustainability aggregations.

 

GenAI also makes it easier for teams to achieve operational excellence. By providing summaries and detailed explanations in natural language, domain experts can better understand the full process picture and make data-driven decisions with beneficial results. This empowers personnel to:

analyze massive datasets quickly and effectively;
identify trends, anomalies and opportunities;
make proactive, impactful and informed decisions.

 These abilities foster operational improvements in production, quality and yield in a variety of industrial sectors.

 

For one 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 within the advanced 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 required pre-analysis using special data science techniques, a task that previously took more than four days to complete with support from an outside coding team. Using the AI Assistant, however, this step now takes just 15 minutes, reducing expenses and providing significantly more time for experts to focus on process analysis.

 

With continuous connectivity to current knowledge bases, GenAI can also enhance workforce empowerment efforts. GenAI-based training provides a flexible and asynchronous format for educating employees, improving convenience and delivering just-in-time guidance for conducting new and/or complex tasks.

 

By streamlining access to modern technologies that make domain experts’ jobs easier and motivate impactful organizational contribution, companies can more effectively attract new talent and retain existing employees’ subject matter expertise.

Implementation challenges

While GenAI promises potential for significant improvements across sustainability, operational excellence and workforce empowerment initiatives, there are notable challenges to integrating this technology into existing workflows that cannot be overlooked.

First, it is difficult to combat preconceived mistrust in GenAI due to initial hallucinations, inaccuracies and other issues encountered in early exposure to the technology. To therefore increase confidence in the technology, GenAI results must be validated. This tool is only as strong as the data and models it is trained and calibrated with, and as the saying goes, garbage in equals garbage out.

 

For industrial organizations, this can mean providing easy access to process data that personnel can use to verify GenAI results. At my company, we provide data contextualization capabilities that help users better determine the relevance of their results, which then helps them perform additional verifications for important decisions.

 

Setting aside time for AI leaders to discuss how the technology benefits users, including relevant use case examples, can also help employees overcome hesitancy and the “organizational inertia barrier” to adopting new or modifying existing workflows. AI tools must prove their worth and effectiveness to employees quickly upon making procedural changes, lest they revert to previous methods.

Additionally, concerns about data privacy and security must be addressed when deploying GenAI in sensitive industries. Because these platforms are open to the internet for model training, developers and implementors must exercise caution to segment confidential information from public-facing components so as not to compromise data.

 

Organizations must also understand and clearly communicate to team members that AI is not a silver bullet for instant solutions. When deployed in the industrial sector, these models require fine-tuning and customization to meet specific needs. Off-the-shelf solutions are not likely to yield optimal or even reasonable results in many environments before making necessary adjustments, additions and tweaks.

Is your team ready?

To assess your organization’s readiness to augment an advanced analytics strategy with GenAI, follow these three key steps:

Evaluate data quality: Relevant and high-quality data is essential for GenAI effectiveness. It must also be accessible to the teams working on solving process problems. Once you are confident in your data quality and accessibility, establish robust data governance practices to ensure continued quality, privacy and compliance to keep ahead of industry regulations.
Assess proficiency in data science and AI: Determine whether staff have the skillsets to develop and maintain GenAI solutions. This requires understanding the business teams that software solutions are targeted toward and their typical workflows. Then, invest in developing and sustaining these skills. By fostering a culture of continuous learning, you can help employees adapt as GenAI evolves.
Examine your infrastructure: Ensure the necessary computational infrastructure and data storage capabilities are in place to support resource-intensive GenAI deployments. Without proper provisions, information- and bandwidth-hungry tools can wreak havoc on ill-equipped facilities.

 As you prepare to deploy, remember to start small. Begin with pilot projects to test GenAI applicability to your company’s specific use cases before scaling up, ideally collecting quick wins along the way. These smaller “low hanging fruit” projects not only accelerate time to ROI, but also establish confidence and provide motivation throughout the organization.

 

By thoughtfully rolling out GenAI-augmented advanced analytics solutions, manufacturers are bolstering efficiency and profitability to keep ahead in increasingly competitive markets.