🤖 Industrial Artificial Intelligence and Human Expertise: Why Is It Still Important?

[Hasan S. Cemkan]

Human-Centric AI: The Power of Expertise​

As artificial intelligence (AI) agents and agent-based workflows become more prevalent in industrial operations, experienced engineers and specialists are reaching retirement age. This situation creates both a challenge and an opportunity for manufacturers in sectors such as oil and gas, energy, and pharmaceuticals: How can we use AI to accelerate work and preserve the human expertise that underpins complex processes?

Human-In-The-Loop (HITL) AI Approach​

Human-In-The-Loop (HITL) AI is an approach that ensures expert judgment remains in the process while AI agents handle analysis and decision support tasks. This method helps organizations capture employees' operational knowledge, making this expertise accessible to future teams.

Preserving Human Expertise as Industrial AI Scales​

Industrial processes generate massive amounts of data, but data alone doesn't always explain what's happening in an operation. For example, while an AI system might detect an unusual equipment signal, an experienced engineer might recognize that this behavior is expected after a recent startup or maintenance event. While AI can quickly uncover the pattern, human expertise provides the necessary context to determine if action is required.

HITL AI brings these complementary strengths together. Depending on the workflow and risk, AI agents can compile process data, identify deviations, and complete specific parts of the analysis. When a decision carries greater risk, the agent can escalate it to an employee with appropriate domain expertise.

Building Decision Capital​

These interactions also create an opportunity to preserve knowledge as experienced employees retire. When AI-powered workflows capture the reasoning that experts contribute during investigations and decisions, organizations can build "Decision Capital": an accumulated history of operational knowledge, context, analytics, reasoning, and actions.

Over time, teams across facilities and sites can leverage this collective expertise when similar conditions arise. Instead of recreating previous analyses, employees can start with what the organization already knows and focus on decisions where human judgment is most critical.

As industrial AI becomes more capable, its value lies not in removing humans from the process, but in expanding the reach of human expertise and making it available to the next generation workforce.