In heavy-asset industries such as oil and gas, precision is crucial. The saying "99% accurate is 100% wrong" reflects this reality. Despite the excitement of new technology, even minor errors can have significant consequences.
For example, an artificial intelligence (AI) system inaccurately predicting a critical machinery component's lifespan could lead to unexpected failures, causing costly downtime and safety hazards.
Kongsberg Digital, an early adopter of Microsoft's large language models (LLMs), has witnessed AI's transformative power firsthand. Over the past 2 years, these LLMs have driven a resurgence in AI interest.
Generative AI's growth presents unprecedented opportunities for the heavy-asset industry, transforming interactions with complex systems. Implementing AI can optimize maintenance schedules, predict failures before they happen, and streamline operations.
The rise of generative AI has also spotlighted the more traditional areas of analytics, classification, prediction, and physics-based simulations. This renewed interest has led the oil and gas industry to look for ways to utilize AI to enhance operational efficiency, reduce costs, and improve safety standards.
However, AI inaccuracies or “hallucinations” are deal-breakers. AI-generated misinformation can mislead decision-makers, potentially resulting in disastrous outcomes. In some cases, using AI is unnecessary and adds little value.
Heavy-asset industries prioritize safety and have historically been conservative in adopting new technologies.
While post-ChatGPT developments are significant, the industry lags due to its zero tolerance for failure. This caution is justified; in environments where lives and substantial investments are at stake, even minor errors can be catastrophic.
Moreover, precision in AI ensures not only safety and efficiency but also drives sustainability.
Accurate AI predictions minimize waste and reduce energy consumption. Inaccurate decisions can lead to excessive energy use and waste, counteracting sustainability goals. AI can lower carbon emissions and reduce the environmental footprint by optimizing energy usage and ensuring regulatory compliance.
Building trust in AI requires responsibility in development and implementation, ensuring security without compromise. We must be transparent about data lineage—a principle that guides our transformative journey. For AI to fully integrate into heavy-asset industries, it must be accurate but also secure and trustworthy. This transparency builds confidence among stakeholders, from engineers to executives.
Implementing AI: The Dos and Don’ts
Prioritize Data Integrity
- Ensure all data used for training AI models are accurate and reliable.
- Poor data quality can lead to erroneous predictions that compromise safety.
- Before you begin, articulate a clear data strategy that includes principles for the scope of data collection, quality, and ownership.
Conduct Rigorous Testing
- Thoroughly test AI systems in controlled environments before deployment to identify and address potential failures.
- Have the adopting organization own this process.
- By doing so, we foster trust and confidence in the solution, improving sustainable uptake.
Keep Human Decision-Making at the Center
- Avoid fully automating critical decisions without expert validation.
- Human oversight is essential to catch anomalies that AI might miss.
- Focus initial adoption on high-frequency workflows where AI and automation can free up humans to focus on higher-impact activities.
Our AI journey highlights the importance of data integrity and precision. Integrating AI is about reimagining processes, not just improving tasks.
For heavy-asset industries, we consider AI adoption to be a marathon. We must avoid rushing, ensuring safety and security.
Even a 1% inaccuracy can cause significant issues. A cautious approach allows us to harness AI's potential while maintaining the highest standards. Here, 99% correct is indeed 100% wrong.
Shane McArdle has served as the CEO of Kongsberg Digital Inc. since September 2022, bringing his experience in digital energy solutions to a broad range of industries. He has been instrumental in driving the development of the firm’s latest software platform that is designed to streamline industrial data workflows. McArdle prioritizes a balanced approach between technology and human input, ensuring that digital tools support professionals in heavy industries such as maritime and energy. His focus on long-term digital strategies reflects his engineering background and commitment to transforming operational practices.