How to Get Started With AI to Demonstrate Business Value In Months
Common Best Practices in Getting Started with Artificial Intelligence for Successful Digitalization
Fortune 500 companies who are succeeding in their digitalization journey are taking a similar approach. They are assigning a Chief Digital Officer (CDO) to assemble an ecosystem that consists of an internal team, potentially a systems integrator to augment their team, and a stack of best-of-breed digital technologies. It is key to understand some of the key attributes of technologies that facilitate and accelerate creating AI applications that deliver value in months.
The AI-enabled software selected by a company should be open in nature to prevent the company from being locked into a technology that is custom-coded by the vendor and requires relying on that vendor for any adjustments or changes. There are two keys to successfully implement AI-driven applications or solutions. First, CDOs must involve the experts that run such operations from the very beginning. Second, they must remember that AI is not a data science project, but instead it is a project that must capture human expertise and combine that with the most relevant data in order to appropriately train the AI algorithms.
One example of AI in action in an industrial setting is in the crude oil refinery area of any oil and gas company. The downstream process of refining crude oil into a finished product involves many potential risks, from equipment failures to unplanned downtime. Crude engineers at oil companies gather knowledge about the refining process — the chemical composition of crude oil, how to treat the oil to avoid corrosion, etc. However, not all crude engineers are experts in hundreds of crude types and when these subject-matter experts leave, the compan y loses the knowledge they possess and struggle to share that information with the broader engineering team.
When engineers don’t have the knowledge necessary to mitigate risks associated with different types of crude, the organization risks hundreds of millions of dollars associated with production loss and equipment failure. In fact, crude corrosion across all oil and gas companies globally, cause $15 billion dollars industry loss. However, the implementation of AI-enabled technology, such as Maana’s Knowledge Platform, enables companies to easily capture (mathematically encode) all engineers’ expertise around the globe on over 200 crude types and create an AI application that provides recommendations on how to mitigate corrosion during the crude refining process, to reduce and eliminate equipment failures.
The key to the success of AI is to ensure that you select a software platform that can be used by your subject-matter experts and not a set of tools that have to be put together by data scientists. The best of breed technologies for AI are designed to be used by subject-matter experts and automate many of the cumbersome tasks of creating models. These models which are developed based on the expertise of the employees that manage those operations,
power the AI apps that provide recommendations to optimize those operations.
In the case of crude oil, implementing Maana allowed the oil company to develop a Crude Flex Knowledge Application, which captures the expertise of subject-matter experts for use by all corrosion engineers. Using the various models stored in the Knowledge Application, the company’s engineers can make decisions that reduce maintenance costs and unplanned downtime.
In order to truly leverage digitalization, companies can simply look at the disruption Amazon has caused to the retail industry. Amazon on a daily basis is creating 6000 decision models per day on its customers and operations versus 200 created by an average retailer.
By establishing a CDO role, making AI an imperative, and selecting software that is capable of combining data with human knowledge, even the world’s largest organizations can create decision models at scale and become truly digital companies, improving operational efficiencies and adding billions of dollars to gross margins.