Energy and trade groups have to make sense of large amounts of data, reporting on trends and delivering strategic insights to a variety of stakeholders, including regulatory agencies, manufacturers, and consumers. How can these organizations fill the skills gap left by analyst vacancies, and deliver faster insights while meeting compliance needs? By using human-in-the-loop (HITL) AI.
The shift to AI-supported workflows is accelerating everywhere. In fact, the global AI-in-energy market was valued at $11.3 billion in 2024 and is projected to reach $54.8 billion by 2030, growing at more than 30% annually (grandviewresearch). Energy and natural resources firms are already on this path — two-thirds (66%) report they are using or actively exploring AI in their analytics workflows (thoughtspot).
These new ways of working enhance data visibility, reporting, and employee satisfaction while forming a new data culture. With HITL, experts make judgment calls and provide context, and they think about what human insights or hypotheses they can test with the help of their trusty digital sidekicks. These humans also ensure the ethical use of AI.
Keep Your People in the Loop
What does it mean to keep someone in the loop? About a dinner plan, a trip to California, or a relative’s health? It doesn’t mean that you give constant updates, just the relevant ones that they can act on.
HITL positions AI and automation to handle routine processes and address the skills gap in junior analyst roles. Here are a few key things to understand about HITL:
- People delegate the work and rebalance the division of labor as needed.
- People are tagged in regularly to do their special work.
- HITL will be a fact of life for early-career junior or administrative employees.
Leaders across the economy need to build skill in junior employees to make sense of complex data sets and follow detailed processes to the letter. However, as we’ve noted in the nonprofit sector, today’s junior employees might not want to take on the grunt work of gathering data, generating charts, and assembling documents. HITL gives every junior analyst an AI coworker to take care of the more mundane steps in their process, which helps to elevate each analyst’s role.
Notably, studies have shown that HITL processes improve model performance more cost-effectively than scaling data alone, particularly in “edge cases” where AI models struggle (arxiv). This means HITL isn’t just a stopgap — it’s a design principle for sustainable AI use. Steve Navarro, President of Mind Over Machines reflects “It is important to note, AI is not here to replace human expertise, it’s here to amplify it. In energy and trade organizations, where the stakes are high and the data is complex, the real breakthrough comes from the partnership between human judgment and AI precision. Human-in-the-loop ensures that analysts don’t just keep up with the pace of change — they lead it.”
A Look Inside the Loop
AI Roles
- Energy Sector:
- AI-assisted data merging for site evaluations.
- SOP chatbots for procedural guidance.
- Trade Associations:
- AI-powered research and benchmarking.
- Agents ensuring data integrity and member engagement.
Human-in-the-Loop Tasks
- Energy Sector:
- Define analytical goals and validate AI outputs.
- Interpret data in context of operational reliability.
- Trade Associations:
- Engage with clients to understand research needs.
- Ensure benchmarking data is accurate and relevant.
The benefits are tangible. For example, AI-enabled predictive maintenance in energy plants can cut unplanned downtime by 20–30%, and AI forecasting improves renewable energy predictions by 25–35%, driving more reliable operations (worldmetrics).
With HITL, humans are liberated to focus time and effort on their unique value. And since they regularly see the benefits of good data practices as HITL processes pick up steam, adoption and consistent use increase. They also recover time to think about and optimize the user experience of reading reports and other outputs of processes they’re involved in. HITL process engineering is the best approach to making AI work for your team.
