Tech Vision: human by design

AI unleashes the next level of human potential for energy companies

Co-authored by Emmanuel Vialle

93% of Energy executives agree that making technology more human will massively expand the opportunities of every industry. And 95% of Energy executives agree that with rapid technological advancements, it is more important than ever for organizations to innovate with purpose.

Year on year, the energy industry makes huge investments in a range of different technologies, introducing new capabilities or increasing the maturity of existing tech. Now, technology has advanced so much that its relationship with humans is at an inflection point. It has become Human by Design.

Technology is no longer just a tool to automate or optimize. It enhances human potential. Human by Design means we no longer develop technology to fully replace humans. We don’t design technology that ignores the human touch, the user experience or human ingenuity. Instead, the human dimension is at the heart of the technology we build.

Energy companies that adopt Human by Design principles create new experiences for their customers and employees. These could be as simple as AI-based agents to help resolve fuel card queries at the service station. As complex as discovering new chemical compounds from hydrocarbons. And everything in between.

A match made in AI

Gen AI is a critical element of Human by Design, and energy companies already recognize its transformative power. In just three years, 93% of energy industry executives anticipate a medium to high impact to their organization’s business processes as a result of generative AI chatbots, with 43% reporting it as high/transformational change.

Transforming our relationship with knowledge

Where will this impact be felt? In all walks of life, Gen AI transforms our relationship with knowledge. But that is especially felt in the way employees interact with corporate knowledge. Soon, Gen AI will transform the future of software and data-driven enterprises, helping software engineers by recommending, even writing, new code.

Today, enterprise knowledge management processes focus on data collection, search and passive knowledge. Gen AI heralds a new era of active knowledge management, creating a more streamlined way for employees to interact with knowledge.

Instead of the list of results returned by traditional online search, Gen AI-based search results take the form of actionable advice or direct answers to questions, backed up by data. Furthermore, AI can automatically tag content, then proactively push it to the users who rely on it.

Improving operations

There are many use cases for Gen AI across exploration and production. It can access blueprints and maintenance records to support plant refresh services in large refineries or platforms. It can support risk service visits and compliance for health and safety.

94% of Energy executives agree the way we interact with data will change

Take pre-maintenance asset inspections. Today, these inspections start with the inspector analyzing large swathes of documentation before the visual inspection occurs. Unlike traditional knowledge management tools, Gen AI can automate the synthesis and analysis of this documentation, saving inspectors significant time. By summarizing this content, an AI agent frees up the inspectors to spend more time on the visual inspection.

There are many other areas where Gen AI can automate highly manual tasks. For example, in process control in a refinery. Refiners rely heavily on sensors and controls to control production. Each one of these devices—known as tags—creates a constant stream of data.

If a tag goes offline, its data must be corrected and uploaded into a data historian. At present, this is a highly manual process but Gen AI, with appropriate human supervision, could automate this process significantly.

Gen AI needs next-generation architecture

The changing way we interact with information requires a transformation in technology architecture. Why? The examples we discussed here rely on both large and small language models.

Each language model is tailor-made to meet a specific purpose. And, large or small, could tap into hundreds of thousands of files in a company. Consequently, Gen AI will force energy companies to modernize their technology architecture. 97% of Energy executives agree that generative AI will compel their organization to modernize its technology architecture.

Meet My Agent

AI-based agents will revolutionize the old world of robotic process automation. Today, software robots are designed to improve the efficiency of a single business process. In the near-future, AI promises the next level of automation, vastly improving the way jobs get done.

AI agents act independently

Soon, whole ecosystems of AI agents could command major aspects of a business. They will act together and—most importantly—complement humans to accomplish specific tasks. 94% of Energy executives agree that leveraging AI agent ecosystems will be a significant opportunity for their organization in the next 3 years.

It is an important distinction between complement and replace. In many instances, humans must make a final decision on an AI agent’s recommendation. AI agents will access knowledge graphs and convert information into actionable insights. However, the complexity of many tasks means humans must lead on decision making.

There is a good analogy between physical cobots on a factory floor and AI agents. On the future factory floor, cobots will understand people and their needs. In an office environment, the cobot is no longer physical, but software based.

Virtual teams need human guidance

AI agents comprise a virtual team, each carrying out specific tasks. Unlike their RPA forebears, they have an additional layer of empathy and can suggest recommendations to a human who makes the final decision.

Appropriate human guidance and oversight is critical because there are risks to using AI. Gen AI suffers from hallucinations. The quality of a recommendation is only as good as the quality of the data, the data sources used, how the data is extracted, the knowledge graph and the recommendation engine. A human must be in place to assess Gen AI’s recommendations, and must be appropriately trained to recognize how that recommendation was generated.

The Space We Need

Applying a Human by Design approach to digital twins provides us with the Space We Need, helping energy companies create value in new realities. What is different to previous efforts is that these new realities provide enriching experiences that have a sense of space and feel lived-in, 94% of energy executives agree. The experiences are diverse and many. They range from learning new skills to advanced simulations of entire asset portfolios.

Digital twins deliver an enriching sense of space

The energy industry is no stranger to virtual reality-based employee training, especially those working in hazardous environments. The difference today is the way spatial computing has become Human by Design. In combination with the next trend, Our Bodies Electronic, it creates a unified experience that fuses fixed sensors on assets, sensors on humans, and digital twin technology. The result is a 3D environment that not only represents a production site in real-time, but also the people working within it. Where previous attempts to fuse the virtual with the physical may have fallen short, The Space We Need takes immersion to the next level.

Creating stimulating virtual environments

Digital twins are now as much about stimulation as they are simulation. They enable a more immersive training experience for staff, and improve the effectiveness of health and safety before anyone sets foot in a hazardous site.

Using further technologies like robot-mounted cameras, 3D point cloud—which provides sub-millimeter precision—and metadata, it’s possible to predict how a gas leak may spread from a failed pipeline seal. Not only do we remove humans from dangerous inspections, we can more accurately identify or predict issues. Spatial computing enhances the user experience in teleoperations by enabling operators to ‘feel’ a remote environment, making teleoperations safer and more efficient.

Augmented with synthetic data

GenAI’s ability to create synthetic data can also augment The Space We Need. For example, an energy company may want to train an AI algorithm to identify smoke by analyzing CCTV footage. But what if it has little or no training data? Gen AI can take existing CCTV footage and enhance it with synthetic data that synthesizes the spread of smoke. In this way, a company can teach a model to detect smoke, even if it has never before recorded the start of a fire.

Our bodies electronic

Our Bodies Electronic marks the beginning of a new human interface. A suite of technologies—from eye-tracking to machine learning to BCI—understands people more deeply, and in more human-centric ways. Critically, humans don’t have to adapt to these technologies. The technologies adapt to them. 97% of Energy executives agree human interface technologies will let us better understand behaviors and intentions, transforming human-machine interaction.

What is critical to widespread acceptance and adoption is that this new human interface be designed safely, credibly with the user in mind. That involves tailoring interfaces to each user’s body language.

If energy companies deploy this correctly, Our Bodies Electronic can significantly enhance many of the use cases we discussed in previous trends. Take asset inspection as an example. The inspector could make use of a cobot or drone, controlled by gestures, voice or even eye movement to improve the resolution of the visual inspection. It extends downstream as well, delivering the next generation of end-customer experience in the fuel station, informed by understanding where people look and walk when moving through the station.

With advanced natural language processing technology, an asset inspector can dictate their findings as they make them. The words they speak can be analyzed in real-time by an AI agent, which can suggest further investigations, suggest potential remedial actions, even issue a work order to a maintenance crew.

Conclusion

As every technology evolves and matures, we always ask the same question: Does it produce the desired results? Over the past ten years, technology adoption has accelerated. 10-year maturity cycles shrank to 5, and are shortening further.

We have learned to react much faster than a decade ago. However, reacting at such a speed means we can sometimes forget about the human element. Human By Design addresses this problem, by placing humans at the center of technology development, regardless of the pace of change.

Many may think AI’s primary use case is to cut costs, particularly in the current economic climate. However, the most successful technology deployments of the past 30 years were always those that took a thoughtful, human approach.

The most successful energy companies in the near future will be those who understand that technology is made and designed by humans.