4 Steps to Showing AI ROI in the Energy and Utility Sector.

One of the most difficult things about persuading leaders to invest in AI is proving return on investment, particularly for conservative industries like utilities and energy. However, we are beginning to see the results of a model that shows the return on investment of AI. AI is changing the way we live and work, but because the changes happen so quickly, it can be difficult to calculate the ROI of AI. The E&U sector is still lagging behind in implementing AI, despite the fact that the trend is generally improving, with 42% of businesses reporting lower costs and 59% of businesses reporting revenue gains.

We will demonstrate how any industry can safely document AI ROI in ways that promote additional investment and even bigger gains by utilizing our client's successful examples of these principles.

We'll tell the tale of one E&U business that is defying the norm in this blog. Our client, a valve inspection business that caters to the oil and gas sector, demonstrates various best practices for implementing AI:

• First, develop a business plan.
• Start with a small project to demonstrate the concept (PoC).
• Make use of your existing tools.
• Another red bullet point
• Consider how any new tools can develop and evolve with your company.

Start With Business Strategy Before You Implement AI

A business plan is the first step in any successful AI project. Which issue are you attempting to resolve? What advantages come from resolving the issue? What role do they play in your long-term business objectives?

Although the issue we set out to resolve for our client was simple, it had a significant strategic impact. The business installs, fixes, tests, and maintains valves that need to adhere to exacting safety and dependability standards. A malfunctioning valve could cause catastrophe.

The difficulty? It takes a lot of time, money, and effort to make sure that every one of a pipeline's thousands of valves operates as intended. Multiple manufacturers will supply valves with different configurations for a single pipeline. Each valve must be inspected on a regular basis after installation by our client's quality control technicians.

The technicians spent a lot of time retrieving specifications for every component of every valve using hard-copy or PDF manuals. In addition to being expensive and time-consuming, the procedure was inaccurate. The same kinds of information would be presented by different manufacturers using different combinations of text, tabular data, and graphics.

Because a distinct quality assurance team was needed to review and rectify the work of the quality control technicians, the maintenance procedure was consequently laborious, expensive, and imprecise.

The following is a strategic way to phrase our client's situation:

• The issue: The inspection procedure is costly, cumbersome, and inaccurate.
• Benefits include increased productivity (pipeline performance and worker time), cost savings (less expensive inspection procedure and failure or repair avoidance), improved safety, increased dependability, and achievement of sustainability targets.
• Long-term Objectives: One step in incorporating AI into the client's supervisory control and data acquisition (SCADA) system is automating the inspection procedure. The AI can assume a self-healing role as it gains knowledge about the pipeline, making corrections to avoid issues. The company can use this capability to run its current pipelines and expand into new areas, such as biofuels.

Long-term Objectives: One step in incorporating AI into the client's supervisory control and data acquisition (SCADA) system is automating the inspection procedure. The AI can assume a self-healing role as it gains knowledge about the pipeline, making corrections to avoid issues. The company can use this capability to run its current pipelines and expand into new areas, such as biofuels.