There’s no doubt that today, utility and energy companies are handling increasingly more (and increasingly more complex - think videos, images, sound, etc.) data, which will inevitably require more complex algorithms. More complex algorithms are able to learn hidden patterns from the data by itself, which is why they are useful - they can deal with problems that a human brain could not understand. And that’s why utilities and energy companies that are able to incorporate AI into their overall business strategy first will get ahead.
But execution is easier said than done, and it requires orchestrated coordination of not only technology, but perhaps more importantly, people and processes as well. Indeed, businesses that have seen the most success in using data to drive profit are those willing to put data in the hands of the many and not just the elite few (like data scientists or even analysts).
That means immediate next steps for companies looking to advance in the race to AI include:
1. Working toward data democratization, meaning alignment of processes around access to data at all levels of the organization. This may mean investing in tools that provide easy (yet controlled) access to data, collaboration, documentation, and AutoML features.
2. Education of all people at the organization surrounding the importance of data-driven decisions as well as possibly the basics on machine learning, deep learning, and data architecture.
3. An investment in tools that enable both data democratization and education, but that are also cutting-edge (think leveraging open source) and elastic, flexible to the needs of both today’s and tomorrow’s organization.