Last week, the annual Digitalization in Oil & Gas conference rolled into Houston, and I was grateful for the opportunity to speak at this always-excellent event hosted by our friends at the Energy Conference Network. The panel was charged to talk about building a digital-ready workforce, and I prepared by summarizing ADI Analytics’ consulting and research on AI from the past 12-24 months, as well as on digitalization over the last decade, for operators, software and digital vendors, and industrial companies in oil & gas, energy, and chemicals.
For years, we’ve heard grand predictions about “digital,” “sensors,” and the “Industrial Internet of Things (IIoT)” running plants and businesses autonomously. But as many in the oil, gas, energy, and chemicals sectors have learned, the reality is far more grounded. The playbook of hyping the impact of new technologies has now come to artificial intelligence (AI). We at ADI Analytics are bullish on AI (and digitalization too) and its impacts across these industries, but we’re not afraid to challenge sweeping predictions of its relevance and impact, especially given the instructive experience of digitalization. From our experience in serving clients on digitalization, the real challenge isn’t the technology itself, but successfully finding and implementing use cases that deliver tangible value.
This is why the most effective approach to digital transformation is a shift from top-down mandates to bottom-up initiatives. The successful identification of AI applications isn’t a C-suite-driven exercise led by a large technology consulting firm. Instead, it’s a process of democratizing the technology and involving rank-and-file employees who have a deep, practical understanding of day-to-day operations and challenges.
National oil companies lead the way
Some national oil companies (NOCs) are proving to be particularly forward-thinking in their investment in AI, especially when it comes to upskilling their staff. Saudi Aramco, for example, has trained more than 6,000 employees on AI beyond its dedicated data scientists. This approach recognizes that subject matter experts who are also trained on AI are crucial for identifying opportunities and executing use cases successfully.
This is a stark contrast to a purely top-down approach. By empowering employees on the ground with new skills, companies can unlock a wealth of insights and operational improvements that might otherwise be missed. This strategy helps overcome one of the biggest challenges to successful AI implementation: data quality and a lack of understanding of its real-world context. Other companies are following suit. India’s ONGC has initiated an AI task force, while Bharat Petroleum (BPCL) has implemented high-impact programs like “Demystifying AI” for over 3,000 employees.
Practical applications and real-world challenges
While the media and vendor narrative often promises revolutionary change, AI applications in the energy and chemical industries are often more about incremental improvements. Companies are leveraging AI in valuable ways (Exhibit 1), such as:
- Research & development (R&D): Modeling experimental conditions and predicting material properties to accelerate research.
- Process design: Accelerating design with more accurate models and real-time process optimization.
- Supply chain: Tracking molecules and simulating network scenarios to optimize inventory and costs.
- Safety: Detecting patterns that precede equipment failures and identifying safety hazards.
Exhibit 1. AI and machine learning are accelerating R&D and process design, driving innovation in the chemical industry.
Despite the potential, finding truly transformative use cases remains a significant hurdle. The gap between expectation and reality is often due to the complexities of real-world implementation, including data quality issues and the need for significant cultural shifts within an organization.
Clariant is a prime example of a company embracing this cultural shift (Exhibit 2). Over a third of its employees use its in-house generative AI (GenAI) assistant, CLARITA, for research and daily tasks. This bottom-up, democratized approach is setting a benchmark in an industry that has been slow to adopt this technology.
Exhibit 2. Clariant is leveraging GenAI to drive company-wide innovation, setting a benchmark in an industry slow to adopt this technology (Clariant, Mizuho, ADI).
Suncor Energy: A case study in operational excellence
A powerful example of a human-centric approach to AI comes from Suncor Energy. The company developed 21 standards for operational excellence based on industry best practices. These standards were created through a bottom-up approach, with subject matter experts and frontline employees working together for over a year to ensure the standards were practical and relevant.
This kind of initiative is perfectly suited for today’s Large Language Models (LLMs), which can accelerate the process of knowledge management and expertise sharing.
- Knowledge consolidation: An LLM can ingest Suncor’s 21 standards, along with the knowledge of its subject matter experts, to create a centralized, easily searchable repository. This makes the vast knowledge base accessible to a wider range of employees through natural language queries.
- Intelligent troubleshooting: A frontline worker could ask an LLM-powered assistant, “What is the standard procedure for a specific valve under these conditions?” The LLM can instantly provide a precise, contextual answer by referencing the internal documentation, saving valuable time and ensuring adherence to best practices.
The Suncor case demonstrates that the real value of these technologies lies in their ability to augment human capabilities, not replace them. By focusing on use cases that empower the rank and file, companies can achieve tangible, bottom-up improvements that lead to real operational excellence.
The path forward
The past decade of digital transformation has taught us a valuable lesson: technology for technology’s sake is a waste of resources. The successful adoption of digital tools, including AI, relies on a strategic, human-centric approach. The companies that are making real progress are:
- Upskilling the workforce: Implementing programs to train existing employees on new technologies.
- Encouraging bottom-up innovation: Empowering a wide range of employees to identify and implement AI solutions.
- Building the right infrastructure: This includes not just the hardware and software, but also ensuring high-quality, clean data, which is a critical requirement for successful AI implementation.
The companies that will succeed in the AI-driven future are those that recognize that the true power of these technologies lies not in the algorithms themselves, but in the people who wield them. It’s about combining AI’s computational power with the deep, practical expertise of the human workforce. This is the kind of practical application that ADI Analytics focuses on, helping clients move beyond the hype to find real value.
– Uday Turaga
About ADI Analytics
ADI is a prestigious, boutique consulting firm specializing in oil and gas, energy, and chemicals since 2009. We bring deep expertise in a broad range of markets where we support Fortune 500, mid-sized and early-stage companies, and investors with consulting services, research reports, and data and analytics, with the goal of delivering actionable outcomes to help our clients achieve tangible results.
We also host the ADI Forum that brings c-suite executives together for meaningful dialogue and strategic insights across the oil & gas, energy transition, and chemicals value chains. Learn more about the ADI Forum.
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