Energy and Utilities Companies Can Build an On-Ramp to the AI Fast Lane With Intelligent Automation

Tyler Chase, Managing Director Energy and Utilities Industry Leader
Evan Campbell, Managing Director Technology Consulting

Like many organizations today, energy and utilities companies are trying to understand advanced artificial intelligence (AI) and identify practical use cases for the technology that will benefit the business and its customers. And like their counterparts in other industries, many of these firms are running short on time – not only to get out of the starting gate with AI but to move their AI projects into the fast lane.

Competing in the Cognitive Age, a report based on a global research survey Protiviti conducted with ESI ThoughtLab, explains that advanced AI is poised to fundamentally change how businesses work to a degree far greater than almost any other new technology since the advent of electricity. And based on our research, we can expect to see more than half of companies around the world garnering significant value from advanced AI within the next two years.

Energy and utilities businesses are likely to be among those organizations. We can count some of these companies among the small fraction of organizations worldwide (16%) already generating significant value from advanced AI. We see leading oil and gas companies making important progress with AI, from designing AI robots for exploration to building AI assistants to help answer customers’ product questions. And utilities companies have made big strides in using AI and machine learning, including natural language processing (NLP), for field service and predictive analysis.

For energy and utilities businesses just starting with AI, it’s time to ramp up efforts. The good news is that there is a way to do so incrementally. As explained below, one significant first step is embracing a level of automation that will help build a path toward “intelligent automation” — a mix of AI and machine learning — that the business can fully leverage when it is ready.

Lack of a Formal Program Is Often a Root Cause for AI Inertia

The path to intelligent automation is lined with small but strategic steps, like robotic process automation (RPA). Businesses can build RPA solutions that use intelligent capture (automating the extraction of data from any format), Python scripts and other AI technologies. RPA can serve as a launch pad for AI in the business and help end users get comfortable with the idea of shifting to intelligent automation.

But many energy and utilities companies are struggling to get started even with these small steps. They’ve purchased RPA or other software and want to implement it in the organization, but they don’t know what to do next, or where. These firms typically lack a clear vision or viable use case for AI, and they don’t have the right people in place to drive the initiative forward and make it successful.

On the flip side, the energy and utilities companies we do see making progress with laying the groundwork for intelligent automation have the following three elements:

  1. A formal innovation program or center of excellence —These companies established their innovation program before they purchased and implemented RPA, AI and/or machine learning technology. With a well-structured program helping to guide their efforts, they are making smart decisions about where to apply advanced technology in the organization.
  • Access to the right talent, both internally and externally —The people aspect of AI success cannot be overstated — and therefore, should not be overlooked. Protiviti’s global AI survey report notes that 90% of companies that are already AI leaders have their own in-house AI development programs. Also, use of external experts and outsourcing can help bridge the talent gap and fuel the company’s AI plans.
  • An overall culture of innovation—The organization, from the top down, has the right mindset about embracing AI and managing the change and disruption it will bring. Everyone across the organization recognizes that innovating must be integral to their job if the business is to achieve sustained innovation excellence with AI and other digital initiatives.

We also see companies making progress with AI being careful not to do too much, too fast, with the technology. That’s good practice; as we caution in Competing in the Cognitive Age, companies should avoid “shooting for the moon” with AI. Instead, they may want to first develop prototypes and proofs of concept to tackle low-hanging fruit that other analytics approaches cannot address effectively.

For example, some areas where we find energy and utilities companies realizing quick wins with intelligent automation include:

  • Invoice and field ticket routing and approval
  • Scheduling and routing of technicians
  • Predictive maintenance
  • Data entry and updates for leases and contracts
  • Purchase order entry into vendor systems
  • Invoice auditing
  • Facilitation of key accounting controls (e.g., three-way matching)
  • Production and metering records maintenance and reporting
  • Materials management and forecasting

As more pilots are developed, the organization’s data needs will become clearer. So, too, will areas where the company should consider making investments and developing infrastructure to support its intelligent automation goals. That includes determining how data will be taken in and from where, deciding how it will be processed, and then building rules and policies for those activities.

Taking a Holistic Approach With AI Use Cases

Another emerging practice we see among companies experimenting with AI is thinking more holistically about use cases. They are identifying use cases where RPA itself is not an answer, but a full AI solution would be too much. So, instead, they are amplifying their RPA investment by adding Python scripts to make the RPA “smarter.” Those efforts are helping them to lay the foundation for implementing NLP and taking other critical steps toward intelligent automation.

While energy and utilities companies are under intensifying pressure to get up to speed with AI, they will be wise to step back and get the big picture on AI for their organizations first. Then, they can create a road map that will lead them toward their digital future and effective use of AI. Of course, companies cannot delay their efforts much longer. As we explain in Competing in the Cognitive Age, businesses do not have the luxury of taking a wait-and-see approach with AI because, if they do, they risk falling behind at their own peril. The most important thing to do is to get started. Even emerging companies in the industry should consider mapping out their AI path sooner rather than later, because it will allow them to create a significant value-add that can potentially attract future buyers.

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